Large language models (LLMs) have revolutionized the field of artificial intelligence, endowing it with sophisticated language understanding and generation capabilities. However, when faced with more complex and interconnected tasks that demand a profound and iterative thought process, LLMs reveal their inherent limitations. Autonomous LLM-powered multi-agent systems represent a strategic response to these challenges. Such systems strive for autonomously tackling user-prompted goals by decomposing them into manageable tasks and orchestrating their execution and result synthesis through a collective of specialized intelligent agents. Equipped with LLM-powered reasoning capabilities, these agents harness the cognitive synergy of collaborating with their peers, enhanced by leveraging contextual resources such as tools and datasets.
While these architectures hold promising potential in amplifying AI capabilities, striking the right balance between different levels of autonomy and alignment remains the crucial challenge for their effective operation.
This paper proposes a comprehensive multi-dimensional taxonomy, engineered to analyze how autonomous LLM-powered multi-agent systems balance the dynamic interplay between autonomy and alignment across various aspects inherent to architectural viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context interaction.
It also includes a domain-ontology model specifying fundamental architectural concepts. Our taxonomy aims to empower researchers, engineers, and AI practitioners to systematically analyze, compare, and understand the architectural dynamics and balancing strategies employed by these increasingly prevalent AI systems, thus contributing to ongoing efforts to develop more reliable and efficient solutions.
The exploratory taxonomic classification of selected representative LLM-powered multi-agent systems illustrates its practical utility and reveals potential for future research and development.

§ INTRODUCTION

In recent years, the emergence and the technological feasibility of large language models (LLMs) have revolutionized the field of artificial intelligence [11, 56, 76, 15, 98].
Pre-trained on vast amounts of text data, these models have catalyzed significant advancements by enabling sophisticated language understanding and generation capabilities, opening doors to a broad range of applications [9, 13, 32].
Yet, despite their remarkable capabilities, LLMs also have inherent limitations.

While LLMs excel at generating outputs based on patterns identified in their training data, they lack a genuine understanding of the real world. Consequently, their outputs might seem plausible on the surface, but can be factually incorrect or even hallucinated [45, 30].
Moreover, despite their proficiency in handling vast amounts of textual information and their rapid processing and pattern recognition capabilities, LLMs struggle with maintaining consistent logic across extended chains of reasoning. This deficiency hinders their ability to engage in a deliberate, in-depth, and iterative thought process (aka slow thinking) [74, 33, 20, 43]. As a result, LLMs encounter difficulties when it comes to handling more complex and interconnected tasks [37, 90].

These limitations of individual LLMs have led to the exploration of more sophisticated and flexible AI architectures including multi-agent systems that aim at accomplishing complex tasks, goals, or problems with the cognitive synergy of multiple autonomous LLM-powered agents [77, 51, 79, 59, 70, 41, 71, 28].
Such systems tackle user-prompted goals by employing a divide & conquer strategy, by breaking them down into smaller manageable tasks. These tasks are then assigned to specialized agents, each equipped with a dedicated role and the reasoning capabilities of an LLM, as well as further competencies by utilizing contextual resources like data sets, tools, or further foundation models.
Taking a cue from Minsky's society of mind theory [48], the key to the systems' problem-solving capability lies in orchestrating the iterative collaboration and mutual feedback between these more or less 'mindless' agents during task execution and result synthesis.

For this purpose, LLM-powered multi-agent systems realize an interaction layer [4]. Externally, this layer facilitates the interaction between the LLM and its contextual environment. This includes interfacing with external data sources, tools, models, and other software systems or applications. These external entities can either generate or modify multi-modal artefacts or initiate further external processes. Internally, the interaction layer allows for organizing the task-management activity by providing a workspace for the collaboration between the LLM-powered agents. Thereby, LLM-powered multi-agent systems are characterized by diverse architectures implementing various architectural design options.

One of the central challenges for the effective operation of LLM-powered multi-agent architectures (as with many AI systems) lies in finding the optimal balance between autonomy and alignment [97, 10, 66, 92, 28]. On the one hand, the systems should be aligned to the goals and intentions of human users; on the other hand, the systems should accomplish the user-prompted goal in a self-organizing manner.
However, a system with high autonomy may handle complex tasks efficiently, but risks straying from its intended purpose if not sufficiently aligned, resulting in unexpected consequences and uncontrollable side effects.
Conversely, a highly aligned system may adhere closely to its intended purpose but may lack the flexibility and initiative to respond adequately to novel situations.
Current systems exhibit diverse approaches and mechanisms to intertwine these cross-cutting concerns [35] throughout their architectural infrastructure and dynamics.

However, existing taxonomies and analysis frameworks for autonomous systems [12, 94, 44, 21, 78] and multi-agent systems [7, 19, 82, 50] fall short in providing means to categorize and understand these challenges and involved architectural complexities posed by LLM-powered multi-agent systems.

This paper aims to bridge this gap by introducing a systematic approach in terms of a comprehensive multi-dimensional taxonomy. This taxonomy is engineered to analyze and classify how autonomous LLM-powered multi-agent systems balance the interplay between autonomy and alignment across different architectural viewpoints, encompassing their inherent aspects and mechanisms.

A simplified representation of the proposed multi-dimensional taxonomy for autonomous LLM-powered multi-agent systems. The x-axis represents the level of autonomy, the y-axis the level of alignment, and the z-axis the four applied architectural viewpoints. Characteristics of LLM-powered multi-agent systems can be assessed and classified by locating them within this taxonomic structure. For an in-depth discussion of the taxonomy, refer to Section <ref>.

The proposed taxonomy is built on the identification and specification of  architectural characteristics of such systems. It aims at understanding the complexities arising from the interplay of interdependent architectural aspects and mechanisms, each characterized by distinct levels of autonomy and alignment.
A simplified overview of the dimensions and levels applied in our taxonomy is represented by the cuboid shown in Fig. <ref>.

First, the synergy between autonomy and alignment manifests as a two-dimensional matrix with multiple hierarchical levels. This matrix captures a spectrum of nine distinct system configurations, ranging from systems that strictly adhere to predefined mechanisms (rule-driven automation, L0/L0) to those that dynamically adapt in real-time, guided by evolving conditions and user feedback (user-responsive autonomy, L2/L2).

Second, these configuration options are not imposed to the LLM-powered multi-agent system flatly. Instead, they are applied to multiple distinct architectural viewpoints [38], such as the system's functionality (goal-driven task management), its internal structure (agent composition), its dynamic interactions (multi-agent collaboration) as well as the involvement of contextual resources such as tools and data (context interaction). Stemming from these four viewpoints, we have discerned 12 architectural aspects, each with distinct autonomy and alignment levels. When integrated into our taxonomic system, they culminate in 108 single configuration options available for further combination.
This granularity facilitates a nuanced analysis and assessment of the system's architectural dynamics resulting from the interplay between autonomy and alignment across the system architecture, laying the foundations for further analysis and reasoning about design decisions.

The contributions of this paper can be categorized as follows:

(1) Architecture Specification: We outline the architectural characteristics of autonomous LLM-powered multi-agent systems and propose a domain-ontology model represented as a UML class diagram structuring the architectural concepts and their interrelations relevant for our taxonomy.

(2) Multi-dimensional Taxonomy: We introduce a comprehensive multi-dimensional taxonomy tailored to analyze and understand how autonomous LLM-powered multi-agent architectures balance the dynamic interplay between autonomy and alignment across different architectural viewpoints. For this purpose, our taxonomy provides hierarchical levels for both autonomy and alignment, which are applied to system viewpoints such as goal-driven task management, agent composition, multi-agent collaboration, and context interaction, thus incorporating a third dimension. Level criteria for autonomy and alignment are specified for several architectural aspects inherent to these viewpoints.

(3) Taxonomic Classification of Selected Systems: We demonstrate the applicability and effectiveness of our taxonomy by assessing and comparing a selection of seven representative autonomous LLM-powered multi-agent systems. This taxonomic classification provides insights into the architectural dynamics and balancing strategies of the analyzed systems. Moreover, it identifies challenges and development potentials, not only concerning the interplay between autonomy and alignment. The application of the taxonomy also serves as a first empirical validation.

Through these contributions, we aim to provide a systematic framework for analyzing, comparing, and understanding the architectural dynamics and complexities of LLM-powered multi-agent systems, thus contributing to the ongoing efforts towards building more reliable and efficient multi-agent systems.

Structure of the Paper.
The remainder of this paper is structured as follows.
Section <ref> discusses related work on taxonomies for intelligent systems and gives an overview of existing autonomous LLM-based multi-agent systems.
Section <ref> outlines the key characteristics and specifies foundational concepts of autonomous LLM-powered multi-agent architectures relevant for the taxonomy.
In Section <ref>, we introduce our multi-dimensional taxonomy, which incorporates specifications of autonomy and alignment levels and their application to architectural viewpoints and aspects of LLM-powered multi-agent systems.
Section <ref> showcases the utility of our taxonomy, as we analyze and categorize selected current multi-agent systems.
In Section <ref>, we discuss the insights gained from this analysis.
Finally, Section <ref> concludes the paper.

§ BACKGROUND AND RELATED WORK

In this section, we discuss the application of taxonomies for autonomous agents and multi-agent systems (Section <ref>) and give an overview of the state-of-the-art of LLM-powered multi-agent systems (Section <ref>).

§.§ Taxonomies and Intelligent Systems

Taxonomies represent structured classification schemes employed  to categorize objects in a hierarchical manner according to specific criteria. They are a popular means for structuring, measuring or comparing various kinds of approaches such as methods, techniques or technologies. They find applications in a wide range of disciplines and domains such as software engineering [81] or explainable artificial intelligence (XAI) [2].

The field of intelligent and autonomous systems spans a variety of configurations and operational structures, with some systems operating as individual entities and others involving multiple interacting agents. Reflecting this variety, the taxonomies proposed over the years have largely followed two main trajectories: those focusing on autonomous systems and those focusing on multi-agent systems.

Taxonomies for Autonomous Systems mainly categorize systems based on the level and type of autonomy, intelligence, learning capabilities, and ability to interact with their environment.
These taxonomies, such as those by [94, 12, 44, 21, 78], are essential for understanding the spectrum of capabilities and complexities inherent to these systems.
In particular, Wooldridge and Jennings [94] present a comprehensive taxonomy that classifies intelligent agents based on key properties such as autonomy, social ability, reactivity, and proactiveness. Their classification sheds light on the independent operational capabilities of single-agent systems. In addition, Brustoloni [12] introduces a taxonomy centered around the idea of autonomy levels, drawing the distinction between autonomous, semi-autonomous, and non-autonomous systems. This provides a valuable lens through which the extent of an agent's independence can be analyzed.
Maes' taxonomy [44] focuses on situating agents within a landscape defined by their reasoning and learning capabilities. The work provides a robust framework for assessing the cognitive dimensions of an agent, ranging from reflex agents to learning agents. Franklin and Graesser [21], in their work, delve into the interaction between autonomous agents and their environment, leading to a taxonomy that is heavily contextual and environmental-dependent.
Lastly, Tosic and Agha [78] put forth a taxonomy that embraces the diversity in the field, offering a multi-faceted perspective on autonomous agents, taking into consideration their design, behavior, and interaction capabilities.
Besides these scientific frameworks, further industry-focused and more pragmatic taxonomies are in use, such as the taxonomy provided by SAE international [29] serving as a foundational standard for self-driving cars with definitions for levels of driving automation; from no driving automation to full automation.

However, while these taxonomies offer valuable insights into the capabilities and behaviors of autonomous systems, they don't inherently address the complexity and nuances involved when multiple agents powered by large language models are working together within a multi-agent architecture. Hence, the need for a dedicated taxonomy for such systems is evident.

Taxonomies for Multi-Agent Systems, on the other hand, extend beyond the confines of individual agent characteristics, integrating the dynamics of interactions and collaborations among multiple agents.
The landscape of multi-agent systems taxonomies provides various works focusing on different aspects of these complex systems [7, 19, 82, 50]. In particular, Bird et al. developed a taxonomy rooted in the communication and cooperation strategies among agents, investigating crucial factors such as communication methods, task decomposition, resource sharing, and conflict resolution [7].
Similarly, Dudek et al. offered a taxonomy specifically for multi-robot systems [19]. This taxonomy, while primarily focusing on robotic applications, can be generalized to other multi-agent systems, considering important aspects like team size, communication topology, team organization, and team composition.
In a different vein, Van Dyke Parunak et al. proposed a taxonomy for distributed AI systems, putting emphasis on environmental aspects and interaction modalities, thus highlighting the importance of the agents' ability to interact with and manipulate their environment [82].
Further broadening the field, Moya et al. proposed a comprehensive taxonomy for multi-agent systems based on characteristics such as the nature of the agents, the environment in which they operate, the communication protocols they use, and the tasks they perform [50]. The taxonomy also thoroughly examined the various types of interactions among the agents, including cooperation, coordination, and negotiation.

While these taxonomies have contributed significantly to our understanding of communication protocols and agent constellation within multi-agent systems, they were developed prior to the advent of large language models (LLMs), and thus do not encapsulate the characteristic challenges associated with LLM-based multi-agent architectures.
In this context, autonomous agents, also as members of multi-agent networks, often have been used as a kind of metaphor [21] for intelligent and interacting components following rule-based communication protocols and bundling a set of specific skills to interact with their environment (e.g., equipped with certain sensors and actuators, cf. multi-robot systems [19]).
LLMs have introduced a new degree of reasoning capabilities, enabling the creation of genuinely intelligent agents operating within collaborative networks in an autonomous manner.

Moreover, while the concepts of autonomy and alignment are often discussed in AI literature [52, 65] and also the system's architecture plays a fundamental role in software engineering [4], none of the existing taxonomies for autonomous systems or for multi-agent systems has so far applied a systematic approach to either investigate architectural aspects or combine the concepts of autonomy and alignment for analyzing the systems.

In the light of these limitations, our work seeks to develop a new taxonomy specifically tailored to LLM-powered multi-agent systems. Our aim is to provide a taxonomy that captures the unique aspects and challenges of LLM-powered architectures, especially with regard to how autonomy and alignment are balanced across architectural aspects and viewpoints, offering a systematic framework for understanding and designing these complex systems.

§.§ Current LLM-based Agent Systems

The advent and widespread use of large language models (LLMs) like GPT-3 [11] have opened up new opportunities for creating increasingly sophisticated and human-like AI systems. These models empower the design of intelligent agents with advanced capabilities to comprehend and generate human-like text, thus enriching the interaction and experience for end-users.
However, the application of LLMs also brings forth several challenges, as highlighted by [32]. Among these, one main challenge lies in handling task complexity, particularly when dealing with intricate tasks that necessitate a well-coordinated execution of numerous interconnected sub-tasks [70] and the interaction with further tools and data.
In response to this, autonomous multi-agent systems utilizing the reasoning abilities of LLMs have emerged. These systems address complexity by intelligently breaking down larger goals into manageable tasks, which are then accomplished by multiple collaborating agents specializing in a specific role and equipped with distinct competencies.
For this purpose, these systems realize an interaction layer [4] providing a workspace for multiple collaborating agents, each connected with an LLM. The reasoning competencies are enhanced, as needed, by the agent's access to contextual resources, such as specific expert tools, data sets, further foundation models, and external applications, which allow the agents to gain information from and to impact their environment by creating or modifying multi-modal artefacts or by triggering external processes. The agents collaborate, bringing their capabilities to bear on the problem, and their results are subsequently combined to achieve the overall goal.

Currently, several projects are established that aim at realizing such autonomous AI architectures for accomplishing complex tasks based on multiple interacting agents and powered by large language models (LLMs).
Exemplary but representative autonomous multi-agent systems are AutoGPT [77], BabyAGI [51], SuperAGI [79], HuggingGPT [70], CAMEL [41], AgentGPT [71] and MetaGPT [28]. For a categorization and comparison of these selected system architectures using the developed taxonomy, see Section <ref>.
Among these systems, we can distinguish those providing general-purpose task management and problem solving with generic agent types and collaboration mechanics [77, 51, 79, 70] and those systems designed for specific application domains with corresponding domain agents and processes, such as for the domain of software development [28, 41, 61].

Some of these recent multi-agent systems as well as further related projects such as Gorilla [60] or Voyager [83] are built upon the LangChain Python framework [14], which allows to realize the aforementioned interaction layer to define agents and chains of tasks as well as the access to and interplay between large language models and contextual resources in terms of data resources (such as vector databases [31]) or various expert tools. For this purpose, predefined components such as agent types and prompt templates, such as for data interaction, can be reused.

Besides these open-source software projects, further scientific projects and approaches are identifiable that leverage multiple agents or personas powered by LLMs for task management and problem solving [88, 26].
Moreover, the interplay between multiple LLM-powered agents is also addressed in other related contexts, such as for simulating the interaction between multiple personas or roles [59, 22], especially with focus on debating and thereby addressing challenges of hallucinations [18] or the degeneration-of-thought (DoT) [42], or for developing conversations and behavior provided by non-playable characters (NPCs) in role-playing video games [17].

A recent survey of LLM-powered autonomous agents is provided by [84], which focuses on investigating and comparing the agents' characteristics and capabilities in terms of profile generation, memory operations and structures, planning, tool integration and learning strategies. Complementing this, another recent survey [95] offers an expansive overview of existing approaches, contextualizing them with foundational technical, methodical, and conceptual paradigms formative for LLM-powered multi-agent systems.

However, as we dive into the specifics of current autonomous LLM-powered multi-agent systems, striking the right balance between autonomy and alignment emerges as a central challenge. These AI systems must navigate a fine line – being autonomous enough to organize the interplay between multiple LLM-powered agents and contextual resources to accomplish complex tasks consisting of various interconnected sub-tasks, but also adequately aligned to the intentions and goals of users. This especially proves challenging, since the specified and prompted user goal might not exactly represent the user's intentions [8], resulting in unexpected consequences and uncontrollable side effects.

Given the exploratory state of the field, current systems exhibit a wide range of architectures, each with its unique blend of autonomy and alignment dispersed across various architectural components and mechanisms.
The diversity in these systems illuminates the different strategies and designs adopted to address this balancing. However, it also signifies the lack of a systematic approach, underscoring the importance of a taxonomy that can provide a structured understanding and comparison of these systems.

§ ARCHITECTURE SPECIFICATION

In this section, we specify architectural foundations relevant for our taxonomy. Section <ref> provides an overview of architectural and behavioral characteristics of autonomous LLM-powered multi-agent systems. Following this, we delve deeper into the architectural key concepts and their interrelationships through a domain-ontology model (see Section <ref>).

Overview of the primary characteristics of autonomous multi-agent systems powered by large language models (LLMs) and enhanced by contextual resources like tools and data. A description of these characteristics is provided in Section <ref>, and for an in-depth exploration of architectural concepts, refer to Section <ref>.

§.§ Characteristics Overview

In the following, we outline the main architectural characteristics of autonomous LLM-powered multi-agent systems, as illustrated in Fig. <ref>.

G Goal-driven Task Management.
Autonomous LLM-powered multi-agent systems are designed to accomplish user-prompted goals or complex tasks. For this purpose, the system employs an interactive and multi-perspective strategy for problem solving, often referred to as deep reasoning or slow thinking [33] enabled by the capabilities of large language models (LLMs) and the advantages of contextual resources. When faced with such challenges, the system adeptly breaks down the complex task into smaller, manageable tasks. These sub-tasks are subsequently distributed among various agents, each equipped with specific competencies.
A crucial aspect of this divide & conquer strategy lies in the effective orchestration of these interconnected sub-tasks and the subsequent synthesis of partial results, ensuring a seamless and cohesive final result.

A LLM-Powered Intelligent Agents.
At the core of these systems, intelligent agents structure the system as the foundational components. Each agent is endowed with a unique set of competencies, which include a clearly defined role, an individual memory, as well as access to further contextual resources, such as data, tools, or foundation models (see below), required for solving the tasks assigned to them.
The backbone of their reasoning and interpretative capabilities is rooted in the incorporation of large language models (LLMs). This enables the agents not only to reflect upon the tasks or to plan and process the assigned tasks efficiently, but also to access and utilize contextual resources, as well as to communicate with other agents.

M Multi-Agent Collaboration.
The interaction layer provides the workspace for a network of such collaborating LLM-powered agents.
While executing the assigned tasks, these specialized agents collaborate with each other via prompt-driven message exchanges to delegate responsibilities, seek assistance, or evaluate the results of tasks undertaken by their peers.
Key to the agents' collaboration is to effectively combine the strengths of each agent to collectively meet the defined goals, exemplifying cognitive synergy. While individual skills are important, the power of these systems emerges from the coordinated efforts of the collective, a concept articulated by Minsky in his idea of the society of mind [48].

C Context Interaction.
Some tasks require the utilization of contextual resources, such as expert tools, data, further specialized foundation models, or other applications. These resources extend their ability to gather environmental information, create or modify artefacts, or initiate external processes.
Leveraging these resources enables the agents to better understand and respond to their operational context and to effectively execute complex tasks.
This capacity for contextual adaptation, augmented by the integration of various resources, contributes to a more versatile and comprehensive system that can address diverse challenges and requirements.

B Balancing Autonomy and Alignment.
The dynamics of LLM-powered multi-agent systems are characterized by a complex interplay between autonomy and alignment. As captured in Fig. <ref>, this complexity can be traced back to the triadic interplay and inherent tensions among the primary decision-making entities: human users, LLM-powered agents, and governing mechanisms or rules integrated into the system.
Alignment, in this context, ensures that the system's actions are in sync with human intentions and values. On the other side of the spectrum, autonomy denotes the agents' inherent capacity for self-organized strategy and operation, allowing them to function independent of predefined rules and mechanism and without human supervision.
Moreover, in systems steered by user-prompted goals, it becomes pivotal to differentiate between generic alignment aspects, in terms of mechanisms predefined by system architects to inform core functionalities, and user-specific preferences customized by the system users themselves.

Triadic interplay and dynamic tensions between the decision-making entities in LLM-powered multi-agent systems.

However, from an architectural perspective, autonomy and alignment transform into cross-cutting concerns [35]. They traverse components and mechanisms across the entirety of the system's architecture, influencing the communication between agents, the interaction with contextual resources, and more.
Achieving a balanced configuration of autonomy and alignment is a crucial challenge, which directly impacts the system's efficiency and effectiveness.

In the following Section <ref>, we elaborate on the architectural details of these characteristics.

Domain-ontology model represented as UML class diagram structuring selected architectural concepts and concept relations relevant for the domain of autonomous LLM-powered multi-agent systems. For a comprehensive exploration of this model, refer to Section <ref>.

§.§ Specification of Architectural Components

Fig. <ref> illustrates a structured overview of selected concepts and their interrelations relevant for the addressed domain of autonomous LLM-powered multi-agent systems in terms of a domain-ontology model.
Domain ontologies, embraced across fields from philosophy to information systems, facilitate a shared understanding of domain-specific concepts [75]. While they aid automated knowledge dissemination among software entities as formal ontologies [23], they are also devised as conceptual models to support human understanding of the addressed domain [36, 24, 25].

Our domain ontology is represented as a conceptual model in terms of a class diagram of the Unified Modeling Language (UML2) [55], which allows for organizing the identified concepts as classes and their relationships in terms of generalizations and kinds of associations with indicated multiplicities (i.e. amount of objects involved in a relationship). For further details on the applied diagram notations, the UML specification serves as a comprehensive guide [55].

The primary objective of the presented model in Fig. <ref> is to mirror architectural concepts especially relevant to our taxonomy's scope. In doing so, it deliberately adopts a high-level view, abstracting from technical details and specifics typical of individual systems to support clarity and accessibility.
For example, while diving into the complexities of the agent's memory usage, as for reflecting and combining task instructions or for planning steps and actions, is undoubtedly worthy of thorough exploration [59, 85], it falls outside the narrow scope of our taxonomy.
This approach ensures that actual multi-agent systems can be regarded as potential instances of this conceptual framework. It's worth noting that this doesn't preclude the addition of more specific technical components and mechanisms as systems evolve.

The domain-ontology model derives from an examination of the code and architectural documentation of several representative multi-agent architectures, especially AutoGPT [77], SuperAGI [79], and MetaGPT [28], the Generative Agents project [59], as well as the LangChain framework [14]. The latter serves as the foundational infrastructure for some of the assessed multi-agent systems (refer to Section <ref>). Through an iterative process, we analyzed these systems and frameworks to understand their components, interactions, and overarching structures. This analysis facilitated the identification and abstraction of recurrent architectural characteristics prevalent among these architectures.

The concepts of the model are arranged into thematic blocks corresponding to the system characteristics identified in Section <ref>, such as G. In the following, we delve into these concepts and their main interrelations. Further details are provided in the domain-ontology diagram illustrated in Fig. <ref>.

G Concepts of Goal-driven Task Management.
Typically, a  initiates system operations via a  through the . Most of these systems employ single-turn prompting to convey intricate  [68]. The prompts can be enriched with detailed instructions, exemplifications like reasoning sequences [90], role specifications, or output expectations [32]. Systems may also permit the definition of  to better align AI operations with user objectives. Besides textual user input, speech, images, videos, or mode combinations are conceivable, for example.
Internally, this user-prompted  (which might represent a directive, problem, question, or mission) undergoes decomposition into  or  to be manageable by the  . These tasks can be interconnected in different ways, such as sequential tasks or graph tasks [70], which requires appropriate task prioritization.
Task decomposition is the first of three core phases within the :

* : Breaking down complex tasks into manageable  and ; optionally resolving dependencies between them, resulting in a prioritized list of .

* : Organizing the distribution and delegation of  among suitable .

* : Evaluating and combining  as well as finally presenting a unified .

Furthermore, each  embodies an  and an .  To maintain transparency and traceability of all  performed, an  captures all relevant action details throughout an activity, while the  distills and retains key insights. In addition, systems might feature a , a repository storing best practices, lessons learned, or reusable knowledge, such as  [91] or specific information like API credentials.
within this activity are delegated to specialized —each characterized by a distinct , , and further competencies. Essential for the actions' success is their interaction with various kinds  —ranging from  and expert  to foundational  (detailed below).
Once all partial tasks are completed, the  are integrated and combined into a  addressing the prompted . This result might also include multiple , encompassing text, graphics, multimedia, and more. The nature and involvement of tools applied, such as  or , can lead to varied , such as triggering external processes. Finally, the , a summation of the result, is relayed to the user through the user interface.

A Concepts of LLM-Powered Intelligent Agents.
Within each , a set of intelligent  collaborate, forming a multi-agent . These agents derive their advanced reasoning capabilities from  (LLMs) [11, 32, 53], which are involved in performing different kinds of , each related to a certain  and/or contributing to its . Each agent is differentiated by its unique  in the activity and possesses an individual —a repository that encompasses condensed experiences and knowledge gained by the agent. It can manifest in multiple formats—be it textual records, structured databases, or embeddings. Oriented to human memorization, in multi-agent systems, we also see a combination of short-term memory (i.e., compressed information transmission via the context window) and approaches for long-term memory [87, 86], such as by leveraging vector databases (see below).
The encapsulated history of the agent's actions might also be chronicled in an .
Furthermore, different generic  types can be distinguished in terms of their roles, and their unique functionalities within the collaborative agent network.

* : These agents are specialized in organizing the processes related to the task-management activity [77, 51, 41].

* : Generating new tasks, which optionally also includes deriving tasks by breaking down complex tasks.

* : Assigning urgency or importance to tasks, which includes to resolve the dependencies between the tasks.

* : Ensuring efficient task completion.

* : These agents are domain-specific experts. They excel in specialized roles within the application domain [59], collaborating with peer role agents when needed. Examples encompass roles in the software-development process, such as project manager, software architect, developer, or QA engineer [28, 41].

* : These agents are tech-savvies, typically tasked with interfacing with technical platforms or development tools. Exemplary technical agents are represented by the  for database interactions or the  for developing Python scripts [14, 28].

An essential distinction to note is the variability in agent memory reliance. While some agents harness the power of memory or an action log, e.g., for reflecting or planning tasks, others function devoid of these recollections. Specifically, for technical aspects or actions that demand an unprejudiced or unbiased lens, agents without memories are often preferred.

M Concepts of Multi-Agent Collaboration.
As detailed above, each  involves a set of multiple collaborating  with different roles and competencies as well as driven by the reasoning capabilities of utilized large language models (LLMs). This reasoning power enables the agents to reflect, plan and process the assigned tasks [85, 59] as well as to interact with other agents [28].
In particular, the  execute different kinds of  which in sum aim at achieving the user-prompted .
In particular, the following sub-types of  performed by the  can be distinguished:

* : Breaking down a task into multiple sub-tasks, optionally ordering and prioritizing the tasks.

* : Defining and generating new tasks.

* : Delegating a task to another agent, addressed as .

* : Actually executing a given task.

* : Assessing the outcomes of a task.

* : Integrating or combining two or more task results.

Thereby, each  can be part of another , which are, however, performed in the context of a certain phase of the  (see Fig. <ref>).
Furthermore, each  can include multiple interactions with an LLM. The LLM's reasoning capabilities are employed in multiple directions within an , such as for reflecting memories and instructions, observing existing results, planning steps and/or weighing options to proceed [85, 59, 28].
For this purpose, an  generated by an  and triggered within a certain  is send to and then processed by the LLM, which generates a  informing and/or guiding the next steps within the triggering action. An action might also include .
Before the LLM receives the , it may undergo  [72].
This process can integrate additional specifics like the aspects or parts of the agent's  or ,  (e.g., data excerpts) acquired from previous , or chosen  prepared and/or adapted for certain kinds of actions [14]. Such agent-driven prompt engineering is pivotal for LLM-powered multi-agent systems.

Direct collaborations involving two or more agents typically rely on prompt-driven communication sequences or cycles. For instance, a  action directed at a  agent might convey information, place a request, initiate a query, or suggest a potential course of action. Subsequently, the  action provides feedback by validating or refuting, and agreeing or disagreeing with the presented results [93].
A  provides a structured framework and methodology for agents' collaboration, guiding the execution of specific  by establishing rules and mechanisms for message exchanges within the multi-agent network [73, 39, 93].
For instance, in LLM-powered multi-agent systems, the following distinct protocols are observable, each built upon the basic mechanisms of an interplay between instruction and execution, with optional subsequent result evaluation:

* Strict finite processes or execution chains with predefined action sequences, interactions between predefined agents, and typically having a well-defined endpoint, which might represent the production of a specific output or artefact [28].

* Dialogue cycles characterized by alternating  and  actions between two agents, creating a feedback loop of instruction and execution [41].

* Multi-cycle process frameworks with interactions between generic agent types, allowing for greater dynamism in agent interactions [77, 51].

In all these exemplary cases, dedicated  are defined and coupled with the corresponding types of . Further details are discussed in Section <ref>.

C Concepts of Context Interaction.
For executing the task-related actions, the LLM-powered agents are able to leverage specialized competencies and further information provided by additional  which can be distinguished into , , and  [70] (see Fig. <ref>).

*  in terms of contextual resources for multi-agent systems can be categorized into the following distinct groups:

* : These tools offer specialized capabilities for probing and analyzing data, allowing agents to derive insights from vast information pools efficiently; such as search engines for the web.

* : These are responsible for interfacing with and executing tasks within other environments, like software applications, ensuring seamless operation across platforms.

* : Enhancing the capacity for logical thought, these tools bolster reasoning capabilities in specialized areas such as computational intelligence. For instance, platforms like Wolfram Alpha empower agents with advanced computational skills.

* : Tailored for software development endeavors, these tools streamline the process of coding, debugging, and deploying solutions within the multi-agent framework.

* : These facilitate interactions with external entities by supporting functionalities like sending and receiving emails, ensuring agents can effectively communicate outside their native environments.

*  types in multi-agent architectures encompass:

* : This refers to data that adheres to a defined model or schema, such as data found in traditional relational databases. It offers predictability and is easily queryable.

* : This data lacks a pre-defined model. An example is content found within PDF documents. For optimal processing by LLMs, unstructured text is typically stored in vector databases like Pinecone or Chroma. These databases support semantic searches through vector embeddings, bridging the gap between structured and unstructured data [47, 31].

* : Beyond just text, this category encapsulates various formats including videos, pictures, and audio. Specialized tools are employed to extract textual information from these formats, making them amenable to processing by LLMs.

* : This data is tailored for particular sectors or areas of expertise. Examples include proprietary company data or external data sources specific to fields like law or medicine.

*  refer to expansive machine learning models trained on vast amounts of data. These models are versatile, suitable for addressing a variety of tasks across different modalities such as language, vision, and audio/speech, as well as combinations thereof [9]. Based on their modalities, we categorize them as follows:

* : These focus primarily on understanding and generating human language.  fall under this category of . While there are general-purpose LLMs available, specialized models tailored to specific domains and tasks also exist, having been trained on corresponding niche data sets.

* : Aimed at processing and understanding images or videos.

* : Specialized in processing and interpreting audio signals, including speech.

* : Designed to handle multiple types of data simultaneously, combining aspects of , , and .

The machine learning landscape shows a multitude of specialized foundation models, with Large Language Models (LLMs) standing out prominently [100]. Platforms like Hugging Face even offer access to numerous models provided by the global machine learning community.

Access to LLMs, as well as associated resources such as tools, foundation models, and external data resources, is typically facilitated through Application Programming Interfaces (APIs) [60]. The access details for these APIs are integrated into the . For instance, these details might be housed within a dedicated  module for streamlined interfacing (see above).
Moreover, multiple of these contextual resources can be combined within a single . For example, a certain expert tool could employ a selected foundation model to analyze a given dataset.
Finally,  might involve the creation or modification of . Beyond mere artefact manipulation, this utilization can manifest as external , such as initiating external processes in other software applications or triggering workflows that influence broader systems.

B Concepts of Balancing Autonomy and Alignment.
Autonomy and alignment represent cross-cutting concerns [35], influencing various architectural concepts and mechanisms. Nevertheless, they also distinctly materialize in specific concepts within our ontology model.
Alignment, on the one hand, primarily manifests through the implementation of dedicated  by the  into the system architecture of the .
These techniques might include foundational infrastructural approaches or procedural controls for system components, framed as constraints, rules, or limitations.
Moreover, alignment can be expressed by the . The user-prompted  can be further refined pre-runtime through supplementary  provided by the  via the . In addition, real-time adaptability can be offered by multi-agent systems, necessitating instantaneous system responsiveness. A more in-depth exploration of this is available in Section <ref>.
However, apart from the alignment achieved within the interaction layer by the multi-agent system, which our taxonomy addresses, there also exist alignment methodologies specifically tailored for the employed LLMs or foundation models  [3, 92].
Autonomy, on the other hand, primarily surfaces from the capability of the multi-agent system to fulfill the designated  autonomously through self-organized strategy and task execution.
Not only do individual collaborative intelligent  utilize the  for reflecting, planning, or performing reasoning-intensive actions pertinent to their roles, but the overarching organization of the , along with other infrastructural or dynamic elements, might also be directed in a self-organized manner, steered by LLM-powered . Further details on this can be found in Section <ref>.
Navigating the complex interplay between autonomy and alignment presents an ongoing challenge for LLM-powered multi-agent systems. Striking the right balance is crucial to ensure an efficient and effective task-execution process that faithfully accomplishes the user-defined .

In the following Section <ref>, we explain how our taxonomy addresses these challenges of analyzing and balancing the interplay between autonomy and alignment across architectural aspects and viewpoints.

§ MULTI-DIMENSIONAL TAXONOMY

In this section, we introduce the system of our multi-dimensional taxonomy, engineered to methodically analyze the interplay between autonomy and alignment across architectures of autonomous LLM-powered multi-agent systems. The taxonomy weaves three crucial dimensions, i.e. levels of autonomy, levels of alignment as well as architectural viewpoints. Together, they form a three-dimensional matrix, serving as a comprehensive repository of architectural design options (see Fig. <ref>).

Section <ref> delves into the complexities of the interplay between autonomy and alignment, exploring how the synergies of different levels of autonomy and alignment can characterize a system's dynamics.

Subsequently, Section <ref>, underscores the importance of incorporating architectural viewpoints into the taxonomic system. Rather than applying the autonomy-alignment matrix flatly, we propose analyzing each architectural viewpoint as well as further inherent architectural characteristics individually.
Such a viewpoint-focused approach allows for a deeper and more nuanced understanding of the systems, reflecting the complexity of their architectural design and resulting dynamics.
Finally, in Section <ref>, we unify these components, mapping the autonomy-alignment dimensions and levels onto the identified architectural viewpoints, thus introducing a third dimension into our taxonomy. Furthermore, we distinguish architectural aspects inherent to these viewpoints and specify corresponding level criteria for both autonomy and alignment.

This framework provides a comprehensive classification of autonomous LLM-powered multi-agent systems, revealing distinct insights into the complexities arising from the interplay of interdependent architectural aspects. Each aspect is characterized by its levels of autonomy and alignment, influencing the systems' behavior, interactions, composition, and interaction with contextual resources.

§.§ Interplay between Autonomy and Alignment

Autonomy and alignment, as interdependent and interplaying concepts, have their roots in management sciences and organizational behavior, playing integral roles in the ways teams and systems function [49, 57]. In these fields, autonomy typically refers to the degree of discretion employees or teams possess over their tasks, while alignment denotes the degree to which these tasks correspond to the organization's overall objectives.

When shifting focus to the AI landscape, the interplay between autonomy and alignment remains pivotal [67, 10]. AI systems, by nature, operate with varying degrees of independence and are often designed to accomplish tasks that are multifaceted, interconnected, and potentially beyond the capabilities of individual human operators. However, complete autonomy can pose risks. If the goals of an AI system deviate from those of its human supervisors, it could lead to unforeseen consequences or uncontrollable side effects. Therefore, controlling the level of autonomy is crucial to maintain the balance between operational efficiency and safety.
As such, understanding and defining the bounds of autonomy and alignment becomes vital to appropriately guide system behavior and prevent unwanted consequences, especially when dealing with autonomous multi-agent systems powered by LLMs.

In order to efficiently address the characteristics of current and forthcoming autonomous LLM-powered multi-agent systems, we adopt a pragmatic and technical perspective on both autonomy and alignment. In the following sections, we explain this perspective and elaborate in detail on the dimensions and levels of autonomy and alignment applied by our taxonomy. Table <ref> gives an overview of the employed levels and the resulting spectrum of potential combinations.

Levels of Autonomy & Alignment    [HTML]c2cfeeL0: Static     [HTML]c2cfeeL1: Adaptive     [HTML]c2cfeeL2: Self-Organizing

[HTML]c2cfeeL2: Real-time Responsive     3 User-Supervised Automation     6 User-Collaborative Adaptation     9 User-Responsive Autonomy

[HTML]c2cfeeL1: User-Guided     2 User-Guided Automation     5 User-Guided Adaptation     8 User-Guided Autonomy

[HTML]c2cfeeL0: Integrated     1 Rule-Driven Automation     4 Pre-Configured Adaptation     7 Bounded Autonomy

Matrix showcasing the interplay between gradations of alignment (vertical) and autonomy (horizontal) in the context of LLM-powered multi-agent architectures. Each cell signifies a unique combination of autonomy and alignment levels. Refer to Sections <ref> and <ref> for details on the applied levels. Section <ref> provides details on the resulting matrix combinations.

§.§.§ Autonomy

The degree of autonomy refers to the extent to which an AI system can make decisions and act independently of rules and mechanisms defined by humans. For LLM-powered multi-agent systems, this translates to a system's proficiency in addressing the goals or tasks specified by the user in a self-organizing manner, adapting and re-calibrating to the complexities of a given situation.
Autonomous multi-agent systems are by nature striving for this end-to-end automatic goal completion and task management from a user perspective.
While automation often gets conflated with autonomy, it's essential to differentiate the two.
Automation pertains to tasks being carried out without human input [12, 29], while autonomy pertains to decisions about tasks being made without human intervention [21, 58, 6]. In the domain of LLM-powered multi-agent systems, we look beyond mere task automation, focusing on how these systems internally manage their dynamics to fulfill user objectives.
Our taxonomy, therefore, distinguishes systems on a spectrum of autonomy. Drawing from the triadic interplay illustrated in Fig. <ref>, on the one end of the spectrum, we see systems that heavily rely on predefined rules and frameworks, set by their human system architects. While they may execute tasks autonomously, their decision-making process is constrained within a fixed set of parameters (low autonomy).
On the other hand, we encounter systems characterized by their ability for self-organisation and dynamic self-adaptation. Rather than relying on hard-coded mechanisms, they harness the power of LLMs to interpret, decide, and act, making them more adaptable to changing situations (high autonomy).

Autonomy Levels.
The levels of autonomy, represented on the x-axis in our matrix (see Fig. <ref> and Table <ref>), articulate the degree of agency of the LLM-powered agents in making decisions regarding the system operation independently from predefined and automated mechanisms.

L0: Static Autonomy - At this foundational level, systems are primarily automated, relying heavily on the rules, conditions, and mechanisms embedded by system architects. The systems follow defined rules and predetermined mechanisms. This, however, includes some degree of flexibility resulting from rule-based options and alternatives. Anyway, the agents in these systems, are not empowered to modify rules during runtime. For instance, their function here is limited to the effective execution of assigned tasks. Depending on the alignment level, this results in Rule-Driven Automation, User-Guided Automation, or User-Supervised Automation (see Table <ref>).

L1: Adaptive Autonomy - Evolving from the static level, systems at this stage possess the capability to adapt their behavior within a structure and procedural guidelines established by the system architects. The LLM-powered agents are capable of adjusting the system's operations within this provided framework (such as flexible infrastructures and protocols) due to the needs of the given application scenarios, but not beyond. Depending on the alignment level, this leads to Pre-Configured Adaptation, User-Guided Adaptation, or User-Collaborative Adaptation.

L2: Self-Organizing Autonomy - At this highest level of autonomy,  LLM-powered agents emerge as the principal actors, capable of self-organization, actively learning and dynamically tailoring their operations in real-time based on environmental cues and experiences. The autonomy lies not in being independent from user intervention, but in being independent of architect-defined rules and mechanisms. However, this might also include highly generic infrastructures that are modifiable by the LLM-powered agents and thus allow self organisation. Depending on the alignment level, this results in Bounded Autonomy, User-Guided Autonomy, or User-Responsive Autonomy.

These levels of autonomy not only apply to the system as a whole, but to architectural viewpoints and involved architectural characteristics (see Section <ref>).

§.§.§ Alignment

In the context of AI, the term alignment traditionally refers to the challenge of ensuring that an AI system's behavior aligns with human intentions, values or goals. This intricate problem, often framed as the control problem, is a cornerstone of AI safety discourse [10, 65].
However, when viewed through a practical lens, especially in the context of autonomous LLM-powered multi-agent systems, the alignment paradigm acquires a more interactive, user-centric perspective [1].
Here, alignment techniques can be seen as a detailed calibration of conditions tied to user-specified objectives or complex tasks. This includes preferences, policies, constraints, and boundaries which collectively steer or regulate the system's trajectory towards achieving its set targets. Importantly, within this framework, alignment is not seen as counter to autonomy. Instead, it acts to complement and refine it, being applicable across various levels of autonomy.

It is also important to note that these alignment aspects are focused on the agent-interaction layer and do not, or at least only indirectly, concern the utilized large language models (LLMs) [3, 92] or other contextual resources, such as foundation models.
However, the agent-interaction layer extends alignment possibilities by integrating rules and mechanisms to control agent interactions, for example, by incorporating real-time monitoring via interceptors [4].  Such measures, as delineated by [40, 27] enable precise control over agent interactions as well as their interactions with LLMs and contextual resources, ensuring that they adhere to predetermined conditions and behaviors.
Moreover, employing methodologies like design by contract [46] further augments this control. Through this paradigm, software components' formal, verifiable specifications, or contracts, can delineate conditions for agent interactions, especially when they engage with foundational resources like LLMs. Such contracts can specify acceptable behaviors, constraints, and criteria, ensuring the system behaves reliably and as intended.
While foundational models like LLMs have their inherent challenges, the agent-interaction layer introduces a distinct dimension of complexity. It is imperative to ensure both are seamlessly and securely integrated, ensuring alignment across all levels of the system.

For our taxonomy, we combine two important dimensions of alignment: its origin and timing, reflecting the dynamic tension between automated alignment mechanisms and human customization, as illustrated in Fig. <ref>.
The origin delves into who dictates the alignment, the system architect or the system user. Meanwhile, timing refers to when the alignment is specified, encompassing phases like pre-deployment, post-deployment but prior to runtime, or even during runtime.
Autonomous LLM-powered multi-agent systems strive for achieving a goal or accomplishing a task prompted by the system user. Given this user-centric model, the alignment techniques that are integrated into the system architecture might address generic aspects, which are not directly related to the nuances and characteristics of specific user goals.
To address this, we've categorized alignment into levels. The base level, or low alignment level, signifies alignment that's already embedded into the system's design by the system architects. This intrinsic alignment sets broad behavioral boundaries without focusing on specific user preferences. On the other hand, the high alignment levels are more adaptable and centered around user-specified alignment. Here, users have the flexibility to set their preferences either before the system enters its runtime or, ultimately, during its active operation. This dynamic range ensures a tighter fit to user objectives, all however built upon mechanisms integrated into the system architecture.

Alignment Levels.
The levels of alignment, represented on the y-axis in our matrix (see Fig. <ref> and Table <ref>), measure the degree to which users of the system can influence or adjust the system's behavior.

L0: Integrated Alignment - At this foundational level, the alignment techniques are built directly into the system's architecture. In such system, alignment mechanisms are static and rule-driven, and cannot be altered by the users. Depending on the autonomy level, this results in Rule-Driven Automation, Pre-Configured Adaptation, or Bounded Autonomy, where user interaction with the system's alignment is not provided (see Table <ref>).

L1: User-Guided Alignment - Evolving from the previous level, the User-Guided Alignment offers a degree of customization. This level empowers users by allowing them to set or adjust specific alignment parameters, such as conditions, rules, or boundaries, before the system starts its operation. These interactions are primarily facilitated via user interfaces designed to capture user preferences in a structured manner. Depending on the autonomy level, this results in User-Guided Automation, User-Guided Adaptation, or User-Guided Autonomy.

L2: Real-Time Responsive Alignment - The highest level of alignment is represented by means to adjust the system's behavior in real-time. Thanks to integrated real-time monitoring mechanisms, the system can actively solicit user feedback user decisions at critical junctures or decision points. This responsiveness enables a high level of collaboration in terms of ongoing feedback between the user and the system. Depending on the autonomy level, this results in User-Supervised Automation, User-Collaborative Adaptation, or User-Responsive Autonomy.

The hierarchical alignment structure mirrors the challenges commonly faced in software development, especially as visualized by the cone of uncertainty [8]. This cone depicts how uncertainties, predominant in the early stages of a project, gradually diminish as developers gain better clarity.
Transferred to the alignment of LLM-powered multi-agent systems, initial alignment challenges are approached with a broad brushstroke by system architects. Their main focus is to ensure foundational system functionality. At this juncture, the specificity of user-driven tasks, with their unique nuances and intricacies, will hardly be anticipated by system architects.
In turn, a user-guided, pre-runtime alignment allows users to specify preferences and limitations based on a more concrete understanding of possible challenges associated to a given task. However, even at this stage, it is barely possible for the user to anticipate all alignment challenges.
Factors like ambiguous prompts, incomplete task specifications, or, in general, unclear expectations, can inadvertently steer the system off course resulting in unintended outcomes and uncontrollable side effects.
Thus, once the system is in operation, such deviations from user's intentions might first become obvious to the user during runtime and require adjustment in real-time. This allows the system to re-align based on immediate user feedback.

These levels of alignment not only apply to the system as a whole, but to architectural viewpoints and involved architectural characteristics (see Section <ref>).

§.§.§ Combinations of Autonomy and Alignment

By combining these two dimensions in our matrix (see Table <ref>), we provide a comprehensive view of the interplay between diverse gradations of autonomy and alignment within LLM-powered multi-agent systems.
As illustrated in Fig. <ref>, departing from static and rule-driven system configurations (automation), this autonomy-alignment matrix captures the progression of dynamism and responsibilities as we move along the axes.
On the y-axis, alignment levels represent the gradation of human users' involvement—from integrated systems where the user's role is passive (L0), to real-time responsive setups demanding active participation (L2). On the y-axis, the autonomy levels signify the evolving capabilities of LLM-powered agents, progressing from static behaviors (L0) to adaptive (L1) and, ultimately, self-organizing mechanisms (L2).
This matrix structure reflects the triadic interplay and dynamic tensions illustrated in Fig. <ref>.
As we delve deeper into the matrix, the challenge becomes evident: ensuring balance between the evolving responsibilities of LLM-powered agents and the goals and intentions by the human users, ultimately resulting in a dynamic collaboration between agents and humans.

Interplay between autonomy and alignment: balancing evolving levels of dynamism and responsibilities of both LLM-powered agents (autonomy) and human users (alignment).

In the following, we detail the resulting nine combinations provided in Table <ref>.

1 Rule-Driven Automation (L0 Autonomy/L0 Alignment): In this configuration, both autonomy and alignment are at the lowest levels. Such system operates based on scripted mechanisms and fixed conditions defined by the system architects. The alignment aspects are integrated into the system during the development stage. At this level, the behavior can not be affected by the user, neither pre-runtime or during runtime. However, this balanced setup is ideal for repetitive, well-defined tasks that require minimal variability or adaptability.

2 User-Guided Automation (L0 Autonomy/L1 Alignment): Here, while the autonomy of the system remains at the lowest level, users can guide the system's behavior within predefined parameters before runtime. The user can not make real-time adjustments, but can influence the system's behavior within the predefined structure. It allows a certain level of customization without granting complete control, which can be ideal for scenarios where user expertise can refine the operation but the task management remains static.

3 User-Supervised Automation (L0 Autonomy/L2 Alignment): This configuration allows the user to supervise and make real-time adjustments to the system, despite the system's autonomy level remaining at the lowest. The user has more control over the system's behavior, being able to guide and correct it as necessary in real-time. This configuration is suitable for tasks requiring real-time user feedback and supervision, but where the processes themselves are performed in a pre-scripted manner.

4 Pre-Configured Adaptation (L1 Autonomy/L0 Alignment): At this level combination, the multi-agent system can adapt its behavior within certain predefined parameters, but the alignment aspects are still integrated into the system during the development stage, with no room for adjustments by the user during runtime. This allows the agents to handle a greater variety of scenarios than strictly rule-driven systems, while still maintaining a clear boundary on its behavior set by the predefined parameters.

5 User-Guided Adaptation (L1 Autonomy/L1 Alignment): Here, the system can adapt its operations within predefined parameters, and the user can also guide the system's behavior within a predefined structure. It offers a balanced mix of system adaptation and user guidance. This combination can be useful when the tasks or environment have some level of unpredictability that requires the LLM-powered agents to adapt within predefined bounds, and where the user’s guidance can inform the system's decisions.

6 User-Collaborative Adaptation (L1 Autonomy/L2 Alignment): This configuration allows the system to adapt its operations and also be responsive to real-time user adjustments. It offers a dynamic interaction between the user and the agents. This configuration is well-suited to environments that are unpredictable and require the system to adapt and respond quickly to the user's real-time instructions.

7 Bounded Autonomy (L2 Autonomy/L0 Alignment): Here, the multi-agent system can self-organize and learn from the environment, but the alignment is integrated during the development stage and cannot be adjusted by users during runtime. This provides the system with a great degree of flexibility to handle complex tasks and environments, while still adhering to a defined set of limitations and constraints specified by the system architect.

8 User-Guided Autonomy (L2 Autonomy/L1 Alignment): At this level, while the system can self-organize and learn from the environment, the user can guide the system's behavior within predefined parameters. This system configuration leverages the agents' self-organizing abilities while allowing user guidance pre-runtime. It combines the strengths of autonomous decision making and learning, with the assurance of user-specified boundaries.

9 User-Responsive Autonomy (L2 Autonomy/L2 Alignment): This is the highest level of autonomy and alignment, where the LLM-powered agents can self-organize, learn from the environment and user's real-time adjustments. It offers a balanced collaborative environment between the user and the agents, being ideal for complex, unpredictable environments where both autonomous strategy and action as well as real-time user-responsiveness are needed.

In the following sections, we explore how the autonomy-alignment matrix can be applied within the context of architectural viewpoints and further architectural aspects inherent to these viewpoints on autonomous LLM-powered multi-agent systems.

§.§ Architectural Viewpoints

Architectural viewpoints are a structured means to analyze and assess complex systems from diverse perspectives focusing on selected aspects and layers of an architecture [4, 16]. Central to these viewpoints is the consideration of stakeholder concerns, which inform and determine the highlighted aspects and their interrelations in each viewpoint. Providing a combined multi-perspective analysis, viewpoints serve as an effective framework to examine the structures and dynamics of software architectures. For our taxonomy, we leverage viewpoints on autonomous LLM-powered multi-agent systems. Rather than mapping the autonomy-alignment taxonomy flatly onto the system, which oversimplifies the multi-faceted nature of these systems, analyzing each architectural viewpoint individually offers a tailored lens, enabling to comprehend the role and impact of autonomy and alignment within the system. Each viewpoint reveals distinct insights into the system's behavior, internal interactions, composition, and context interaction, leading to a more nuanced and comprehensive classification [64].

Architectural viewpoints oriented to the 4+1 view model of software architecture [38] applied to autonomous LLM-powered multi-agent systems.

§.§.§ Applied Viewpoints

For our taxonomy, we orient to Kruchten's renowned 4+1 view model of software architecture [38], an established standard viewpoint model for software architecture, adapting it to suit the architectural characteristics of LLM-powered multi-agent systems (see Section <ref>). Our taxonomy encompasses the following four architectural viewpoints on these systems (refer to Fig. <ref>):

G Goal-driven Task Management (Functional Viewpoint): Kruchten's functional viewpoint refers to the system's visible functionalities as experienced by its users [38]. In the context of autonomous LLM-powered multi-agent systems, we see  as a manifestation of this functional viewpoint. It entails the system's capabilities and mechanisms to decompose user-prompted goals or complex tasks into smaller, more manageable tasks, and subsequently, orchestrate task execution, combine the results, and deliver the final result forming the response for the user (see Figs. <ref> and <ref>).

A Agent Composition (Development Viewpoint): According to Kruchten, the development viewpoint is primarily focusing on the system's software architecture, the breakdown into components, and their organization [38]. In our context, we interpret this as , focusing on the system's internal composition, particularly the assembly and constellation of agents. It includes the types and roles of agents, their memory usage, the relationships between agents (see Figs. <ref> and <ref>).

M Multi-Agent Collaboration (Process Viewpoint): Kruchten's process viewpoint concerns the dynamic aspects of a system, specifically the system procedures and interactions between components [38]. We apply this to the  in our model, emphasizing the collaborative task execution and interactions among agents. This encompasses the application of communication protocols, the dynamics of actions management, such as the actual task execution, mutual task delegation, as well as the evaluation and merging of task results on agent level, as well as the management of communication components such as prompts and prompt templates (see Figs. <ref> and <ref>).

C Context Interaction (Physical Viewpoint): According to Kruchten, the physical viewpoint involves the system's mapping to physical resources [38]. We extend this to , focusing on the system's interaction with the external environment. It includes how the system acquires, integrates, and utilizes contextual resources such as external data, expert tools, and further foundation models as well as the organized distribution and utilization of contextual resources within the agent network (see Figs. <ref> and <ref>).

§.§.§ Viewpoint Interdependencies

To effectively design and understand autonomous LLM-powered multi-agent systems, it's essential to recognize the relationships and interdependencies between architectural components and viewpoints [99, 64].
Fig. <ref> illustrates these interrelated architectural viewpoints for multi-agent architectures. The figure includes use dependencies between the viewpoints, denoted as dotted lines indicating the directions of usage [55].
These dependencies arise from the interconnected nature of these viewpoints, as they collectively shape the behavioral features provided by the system, here expressed in terms of the  viewpoint.

The interplay between architectural viewpoints is notably influenced by the autonomy levels of the systems. With regard to the levels of autonomy, we can distinguish the following two types of dependencies between architectural viewpoints, i.e., availability-driven dependencies for low-autonomy systems and requirements-driven dependencies for high-autonomy systems. Fig. <ref> illustrates the two types in a simplified manner.
For further details on dependencies between architectural aspects inherent to the viewpoints, also see the feature diagram in Fig. <ref>.

Types of dependencies distinguished by different levels of autonomy provided by LLM-powered multi-agent architectures.

Availability-driven Dependencies (Low-Autonomy System).
For low-autonomy multi-agent systems, as depicted in Fig. <ref> (a), the architecture operates predominantly under pre-established automation. In these systems, functionality largely relies on pre-configured rules and mechanisms. Thus, the functionality of such multi-agent system is contingent upon the predefined capabilities of the system processes, which are defined by the structure of the system and the resources available.

* In such systems,  depends on all other dimensions, as it represents the culmination of the system's operations in terms of the key functionality as perceived by the user. The capabilities regarding the decomposition and orchestration of task execution towards completing the prompted task are essentially influenced by the predefined composition and constellation of agents (such as competencies, roles, types, and network), their scripted mode of collaboration for task execution, and the rule-based integration and utilization of contextual resources.

* In turn,  derives its operational modus from the foundational structures established by the  and . The mode of collaboration for task execution among agents is dictated by predefined characteristics and competencies (types, roles) of the agents involved and their relationships and organization as network (), as well as by the accessibility in terms of the effective integration and utilization of contextual information, tools and models to execute the given tasks ().

* Finally,  also relies on the availability of contextual resources (Context Interaction). The types and number of agents needed in the system as well as their roles and competencies are directly influenced by the contextual environment (e.g., the availability, accessibility, and quality of data, foundation models, and expert tools) used by the system and how they are utilized within the task-management activity.

Requirements-driven Dependencies (High-Autonomy System).
In turn, high-autonomy multi-agent systems, illustrated in Fig. <ref> (b), have the ability to self-organize. In these systems, the architectural infrastructure and dynamics as well as the context interaction are self-organizing and thus capable of adapting their capabilities to the needs and requirements set by a given goal. Thus, compared to systems with low autonomy, there is an inverse dependency relationship.

* In highly-autonomous multi-agent systems, the user-prompted goal delineates the requirements, charting the course for the entire architectural edifice of the system.
All other viewpoints adapt to the envisioned functional behavior expressed as . Based on the complexity of the goal, its decomposition into tasks and their distribution, the other architectural aspects inherent to the three further viewpoints undergo adaptations to fit the needs of the given situation.

* In addition, , encompassing agents' roles, types, and their memory usage, as well as , including the integration and utilization of resources, adapt to the requirements set by the modes of collaboration to tackle the assigned tasks, including the communication protocol ().

* Finally, also  sets the requirement for the adaptation of . The specific roles of agents within the system mandate particular resource integration. For instance, an agent with analytical responsibilities might necessitate the inclusion of specific data streams or computational tools.

Intertwined Dependencies (Mixed Autonomy Levels).
The two distinguished types, availability-driven and requirements-driven dependencies, address the challenge of interconnected architectural viewpoints in an illustrative, but simplified manner.
On the one hand, the viewpoints of a multi-agent system might provide different autonomy levels; on the other hand, also the aspects or mechanisms within a viewpoint might be on different levels of autonomy. Both cases result in intertwined dependencies.
It is important to note that the autonomy levels set for one viewpoint or aspects can have an impact on others viewpoints or aspects due to their interconnected nature (see above).
The intertwined dependencies might prove risky. They can introduce complexities and unpredictabilities, potentially jeopardizing system efficiency and effectiveness. It underscores the necessity of incorporating robust control mechanisms to navigate and manage these interdependencies, which is illustrated by the following illustrative example.

Example. Consider a practical scenario where an autonomous LLM-powered multi-agent system is operating in the following dynamic environment:

* Decomposition Dynamics: Within the  viewpoint, tasks are dynamically decomposed into sub-tasks based on user requirements, and this decomposition operates with a L2 autonomy level, which signifies a self-organizing manner.

* Agent Collaboration Dynamics: Similarly, the  viewpoint, which encompasses how agents collaborate for task execution, operates on the same L2 autonomy. Agents decide on-the-fly how to interact, delegate tasks, and merge results.

* Contextual Interaction Limitation: Contrasting the above, the  viewpoint is constrained by L0 autonomy level. Here, the system's access and interaction with the external environment (contextual resources) are limited by predefined rules. The system cannot autonomously decide to reach out to new resources or modify the way it interacts with existing ones.

Given these conditions, a potential issue arises: As tasks are decomposed and agents plan their collaborations, they might, based on their L2 autonomy, decide to utilize certain contextual resources. However, when it's time to access these resources, they might find them inaccessible due to the L0 constraints in the  viewpoint. This discrepancy in autonomy levels can cause operational dead-ends. For instance, an agent might anticipate using an external data source to complete its task, but the L0 constraints prevent it from accessing that source, leaving the task incomplete.

Such issues highlight the importance of having robust control mechanisms in place that can preemptively identify and mitigate these discrepancies, ensuring smooth system operations.

For a detailed illustration of dependencies between viewpoint-specific aspects, refer to Section <ref>.

Matrix #     Autonomy-Alignment Levels    [HTML]c2cfeeG Goal-driven Task Management     [HTML]c2cfeeA Agent Composition     [HTML]c2cfeeM Multi-Agent Collaboration     [HTML]c2cfeeC Context Interaction

[HTML]c2cfee1   [HTML]c2cfeeRule-Driven Automation: Static & Integrated (L0 & L0)     Rule-driven task management.      Rule-driven agent composition and constellation.     Rule-driven collaboration protocols.     Rule-driven interaction with contextual resources.

[HTML]c2cfee2   [HTML]c2cfeeUser-Guided Automation: Static & User-Guided (L0 & L1)     User-guided task management.     User-guided agent composition and constellation.     User-guided collaboration protocols.     User-guided context integration and utilization.

[HTML]c2cfee3   [HTML]c2cfeeUser-Supervised Automation: Static & Real-Time Responsive (L0 & L2)     Task management adjusted during runtime.     Agent composition and constellation adjusted during runtime.     Agent collaboration adjusted during runtime.     Context integration and utilization adjusted during runtime.

[HTML]c2cfee4   [HTML]c2cfeePre-Configured Adaptation: Adaptive & Integrated (L1 & L0)     Adaptive task management with predefined options.     Adaptive agent composition and constellation with predefined flexibility.     Adaptive collaboration protocols.     Pre-integrated adaptive contextual resources.

[HTML]c2cfee5   [HTML]c2cfeeUser-Guided Adaptation: Adaptive & User-Guided (L1 & L1)     User-adjusted adaptive task management.     User-adjusted adaptive agent composition and constellation.     User-adjusted adaptive collaboration.     User-adjusted adaptive context integration and utilization.

[HTML]c2cfee6   [HTML]c2cfeeUser-Collaborative Adaptation: Adaptive & Real-Time Responsive (L1 & L2)     Adaptive task management adjusted during runtime.     Adaptive agent composition and constellation adjusted during runtime.     Adaptive collaboration adjusted during runtime.     Adaptive context integration and utilization adjusted during runtime.

[HTML]c2cfee7   [HTML]c2cfeeBounded Autonomy: Self-Organizing & Integrated (L2 & L0)     Task management organically based on current needs.     Agents self-organize based on current scenario.     Collaboration strategy evolves organically.     Agents select from a pool of contextual resources.

[HTML]c2cfee8   [HTML]c2cfeeUser-Guided Autonomy: Self-Organizing & User-Guided (L2 & L1)     User-guided self-organizing task management.     User-guided agent self-organization.     User-guided collaboration evolution.     User-guided self-organized selection from contextual resources.

[HTML]c2cfee9   [HTML]c2cfeeUser-Responsive Autonomy: Self-Organizing & Real-Time Responsive (L2 & L2)     Self-organizing task management adjusted during runtime.     Agent self-organization adjusted during runtime.     Collaboration evolution adjusted during runtime.     Self-organized selection from contextual resources adjusted during runtime.

Mapping autonomy and alignment levels (vertical, #1–9 resulting from Table <ref>) to architectural viewpoints (horizontal) on autonomous LLM-powered multi-agent systems resulting in 36 viewpoint-specific system configurations. A detailed explanation of the autonomy and alignment levels is provided in Section <ref>. For an overview of the applied viewpoints, refer to Section <ref>.

§.§ Interplay of Autonomy and Alignment in the System Architecture

As already illustrated, both autonomy and alignment serve as cross-cutting concerns [35] impacting the operational efficiency of various architectural aspects across LLM-powered multi-agent systems. Thus, in the following, we map our matrix of autonomy and alignment levels onto the architectural viewpoints. This projection crafts a three-dimensional matrix, offering a prism through which these systems can be analyzed and categorized (also see Fig. <ref>). In Section <ref>, we give systematic overview of the resulting viewpoint-specific combinations of autonomy and alignment levels. Section <ref> details the architectural aspects associated to these viewpoints and specifies corresponding level criteria that establish the foundation for the taxonomic classification.

§.§.§ Mapping Autonomy-Alignment Levels to Viewpoints

Table <ref> showcases the interplay of autonomy, alignment, and the distinct architectural viewpoints. It applies the autonomy-alignment matrix, as illustrated in Table <ref>, to the identified architectural viewpoints inherent to autonomous LLM-powered multi-agent systems. Each cell in this matrix signifies a unique architectural design choice, representing a distinct system configuration.
The architectural viewpoints (horizontal; see Section <ref>) are categorized into , which highlights the system's functionalities; , emphasizing its intrinsic structure; , denoting the dynamics of agent interactions; and , detailing the system's rapport with its external environment in terms of data and tools.
Alongside these viewpoints, the nine combinations of autonomy and alignment levels (vertical; see Section <ref>) describe the system's behavior. Autonomy ranges from  to , determining the system's degree of self-organization. Meanwhile, alignment varies from  to , capturing the depth of human influence over the system's operations. Combining these dimensions results in 36 system architectural design options available for configuring multi-agent systems.

In the following Section <ref>, we explore further viewpoint-specific aspects and their interdependencies, in order to derive level criteria for the taxonomic classification.

§.§.§ Viewpoint-specific Aspects and Level Criteria

As outlined above, architectural viewpoints provide means to analyze certain aspects and aspect relations of the system's architecture in a multi-perspective manner [64]. Drawing from the domain-ontology model (Fig. <ref>), we now systematize the viewpoint-specific aspects employed in our taxonomy. Subsequently, we specify level criteria for autonomy and alignment corresponding to each aspect. Furthermore, we outline the main interdependencies among these aspects.

Feature diagram showcasing the taxonomic structure. Each viewpoint integrates autonomy and alignment levels (a). The diagram further illustrates viewpoint-specific aspects and mechanisms (b–e) alongside the adapts-to dependencies among them.

Fig. <ref> gives an overview of our taxonomy's characteristics, structured through a feature diagram [5, 69].
Employed predominantly in software engineering, feature diagrams visually express feature models,
which aim to organize the hierarchical structure as well as dependencies among system features.

In particular, Fig. <ref> (a) structures the viewpoint-specific taxonomic structure. Each of the four integrated viewpoints provides a certain combination of autonomy and alignment levels. As illustrated in Figs. <ref> (b–e), this structure is refined by viewpoint-specific aspects and their interdependencies in terms of requirements-driven dependencies (adapts-to), presuming a high-autonomy system configuration, as discussed in Section <ref>. These dependencies suggest that the capabilities of a dependent aspect evolve in line with the needs and stipulations of the aspect it points to. In turn, also these viewpoint-specific aspects can be assessed by the autonomy and alignment levels, resulting in a more nuanced taxonomic classification.

Across the four distinct viewpoints, a total of 12 characteristic aspects are identified (as illustrated in Fig. <ref>). Each of these aspects can be assessed and classified by its corresponding autonomy and alignment levels, yielding 9 possible configuration options per aspect (detailed in Table <ref>). Thus, given the viewpoints \(V_1, V_2, V_3, V_4\) with respective aspect counts \(A_1 = A_2 = 3\), \(A_3 = 4\), and \(A_4 = 2\), and a level count \(L = 3\) for both autonomy and alignment, we define:

\begin{align}
T_A &= \sum_{i=1}^{4} A_i, &\text{(Total Aspects)} \\
S_C &= L^2, &\text{(Single Configuration Options per Aspect)} \\
T_{SC} &= T_A  S_C, &\text{(Total Single Configuration Options)} \\
T_{CC} = (L^2)^{A_1 + A_2 + A_3 + A_4} &= S_C^{T_A}. &\text{(Total Combined Configurations)}
\end{align}

Using the provided values, we find \(T_{SC} = 108\) and \(T_{CC} = 9^{12} \approx 282 \times 10^9\).

In sum, mapping the autonomy-alignment matrix onto the identified aspects, our taxonomy captures 108 distinct single configuration options. When considering all possible combinations of these configurations, we arrive at a total of \( 9^{12} \), which equates to roughly 282 billion combinations available for configuring LLM-powered multi-agent architectures. This underscores the complexity challenge posed by such systems, further accentuated by the various options for intertwined dependencies, as detailed in Section <ref>.

In the following, we outline these viewpoint-specific aspects, drawing from the architectural specifications detailed in Section <ref> and define corresponding criteria for the levels of autonomy and alignment.

G Aspects and Levels of Goal-driven Task Management.

Taxonomic aspects of  comprise the three constituting phases:  (how the goal or complex task is broken down into manageable sub-tasks),  (how these tasks are distributed among the LLM-powered agents), and  (how the results of the tasks are finally combined); refer to Fig. <ref> (b).

Level Criteria:

* Static Autonomy (L0): At this level, we observe scripted processes and automated mechanics with rule-based options and alternatives for the task-management activity, including the phases of task decomposition, distributing and orchestrating the execution of single tasks, or combining their results. These scripted automated processes might demonstrate variability and flexibility including iterations based on predetermined mechanics and conditions. However, this level also includes strict processes or execution chains with no variations.

* Adaptive Autonomy (L1): Here, the system provides predefined but adaptive procedures for the phases of the task-management activity. Based on these predefinitions integrated into the system's design and implementation, the LLM-powered agents are vested with certain autonomy to adapt the managing and controlling of the task-management processes. For example, within a defined framework, the agents are involved in managing the task decomposition, the distribution to other agents, or decisions about the synthesis of results. For this purpose, also patterns or prepared mechanics are reused.

* Self-Organizing Autonomy (L2): This level embodies the LLM-powered agents' capability to architect and implement their own strategy for deconstructing and solving problems due to the characteristics or complexity of a given goal. This might also include high-level generic frameworks scaffolding the agents' interactions and processes, but leaving space to LLM-powered agents for effectively self-organizing the phases of the task-management process.

* Alignment Levels: At this juncture, alignment can be seen in terms of information and constraints regarding the task-management activity, especially regarding the mechanics in the decomposition, orchestration or synthesis of sub-processes, e.g., decomposition depth or consensus options for the total result. The alignment is either integrated into the system's design (L0), configurable by the user before runtime (L1) or adjustable during runtime (L2).

M Aspects and Levels of Multi-Agent Collaboration.

For the taxonomic classification within , we consider  (how the collaboration and dialogues between the agents are managed),  (how prompts are applied during collaboration and executing the actions), and  (how the different kinds of action, such as the delegation or actual execution of tasks, or the result evaluation, performed by the agents are managed); see Fig. <ref> (c)).

Level Criteria:

* Static Autonomy (L0): At this level, collaborative actions and interactions among agents adhere to a fixed script or set of rules. The communication protocols, prompt use and augmentation, as well as the management of actions are pre-defined and don't adjust dynamically based on agent inputs or environmental changes. Agents communicate, delegate tasks, execute instructions, and evaluate results based strictly on established, non-adaptable guidelines. Variability in the collaboration process is minimal and doesn't account for unforeseen scenarios or complexities.

* Adaptive Autonomy (L1): This level introduces adaptability of collaboration aspects for LLM-powered agents based on predefined mechanisms. For example, the communication protocol, the prompt templates, or the management of the agent actions are modifiable. While the foundational mechanisms are preset, the LLM-powered agents can autonomously select and adapt them due to the evolving requirements of the given scenario. For this purpose, they might reuse prepared mechanisms or patterns.

* Self-Organizing Autonomy (L2): Agents operating at this level showcase the capability to independently strategize their collaboration for task execution. Driven by the specific demands of the set goals and task complexities, these LLM-powered agents actively plan and execute collaboration strategies that best address the scenario at hand. For instance, LLM-powered agents can self-organize protocols for collaboration, mechanisms of prompt engineering and negotiate collaboratively the execution of actions among the agent network.

* Alignment Levels: Relevant considerations include information and constraints linked to collaboration mechanisms and patterns between agents, the specification of prompt templates, constraints for prompt augmentation, or preferences for the execution of actions. These components can be either embedded within the system's design (L0), made available for user configuration before runtime (L1), or be amenable to real-time adjustments (L2).

A Aspects and Levels of Agent Composition.

The aspects of  applied by the taxonomy comprise  (how the agents are created, including the strategies and mechanisms employed),  (how agents' roles are specified),  (how the agents utilize their memory, i.e., how information is summarized and stored, or how memory is used for reflecting instructions or planning actions), and  (how the constellation and relationships among agents are managed); refer to Fig. <ref> (d).

Level Criteria:

* Static Autonomy (L0): This level features a predefined and rule-driven composition and constellation of agents. Rules and mechanisms manage the creating of agents, select the agent types, and delineate their roles and competencies. Memory utilization follows predefined mechanisms, as well as the relationship between agents.

* Adaptive Autonomy (L1): While a system at this level provides predefined structures, it grants a degree of flexibility, permitting LLM-powered agents to adapt their composition and constellation within the given framework and due to given scenarios. For example, agents can replicate instances, their competencies are extensible and roles and further attributes (such as the size or compression mode for the agent memory) can be modified. Agents can modify or extend existing relationships, e.g., by connecting with further agents.

* Self-Organizing Autonomy (L2): LLM-powered agents operating at this level exhibit the ability to autonomously define and generate types and establish collaborative networks. The impetus for such self-organization arises from an acute understanding of the demands and nuances of the given scenario. Instead of adhering to predefined agent types and roles or relationships, agents dynamically constitute and organize based on real-time needs.

* Alignment Levels: Pertinent to agent composition are information and constraints regarding their creation, types, roles, and competencies. Further, the manner in which agents interrelate and how they are structured within the network holds significance. These mechanics and configurations can be either deeply embedded into the system's design (L0), be made configurable by the user before runtime (L1), or be dynamically adjustable during system operation (L2).

C Aspects and Levels of Context Interaction.

For , the taxonomic aspects comprise ( (how the integration of contextual resources in terms of data, tools, models, and other applications is achieved), and  (how these resources are actually utilized for executing tasks); refer to Fig. <ref> (e).

Level Criteria:

* Static Autonomy (L0): At this level, contextual resources, including data, expert tools, and specialized foundation models, are rigidly integrated based on the system's initial design. Their utilization is organized by predefined rules and patterns relating scenarios and resource application. However, this level also includes the case that certain or any resources might not be available for use.

* Adaptive Autonomy (L1): Certain contextual resources are pre-integrated, but the system provides adaptive mechanisms usable by LLM-powered agents for integrating missing resources when needed. To this end, access to certain APIs might be prepared. Based on predetermined mechanisms, the LLM-powered agents can flexible determine how to best utilize and combine these provided resources, tailoring their approach to the unique requirements of the given scenario.

* Self-Organizing Autonomy (L2): LLM-powered agents possess the autonomy to interface with a diverse pool of contextual resources (cf. Hugging Face). They can discerningly select, integrate, and harness these resources based on the objectives at hand and the specific challenges they encounter.

* Alignment Levels: Factors to consider encompass information and constraints pertaining to the integration and application of contextual resources. These may include specifications or guidelines on which resources to leverage, when and how to integrate them, any limitations on their utilization, and more. These specifications or guidelines might be built into the system's design (L0), made available for user modification prior to runtime (L1), or even be adapted in real time (L2).

In the following Section <ref>, we explore the application of our taxonomy to real-world LLM-based multi-agent systems.

§ CLASSIFICATION OF SELECTED SYSTEMS

In order to demonstrate the practical utility of our taxonomy, we analyze and classify selected existing autonomous LLM-powered multi-agent systems. We have chosen a set of seven state-of-the-art multi-agent systems for this assessment: AutoGPT [77], BabyAGI [51], SuperAGI [79], HuggingGPT [70], MetaGPT [28], CAMEL [41], and AgentGPT [71]. Each of these systems is maintained and available as open-source project. For basic information on these and further LLM-powered multi-agent systems, refer to Section <ref>.
For each selected system, we gathered relevant information by examining the technical documentation and research papers, where available, as well as reviewing the code base. We further engaged with each system to explore its real-time functionalities, with emphasis on alignment mechanisms available before and during runtime.

In the following sections, we first report on the results of analyzing and classifying the selected systems (Section <ref>). Then, we compare and interpret the results in Section <ref>.

[c]2cmLLM-powered Multi-Agent Systems     6c|[HTML]c2cfeeGoal-driven Task Mgmt.     6c|[HTML]c2cfeeMulti-Agent Collaboration     8c|[HTML]c2cfeeAgent Composition     4c|[HTML]c2cfeeContext Interact.

2c|[HTML]c2cfeeDecom     2c|[HTML]c2cfeeOrch     2c|[HTML]c2cfeeSynth     2c|[HTML]c2cfeeCommP     2c|[HTML]c2cfeePrEng     2c|[HTML]c2cfeeActM     2c|[HTML]c2cfeeAGen     2c|[HTML]c2cfeeRoleD     2c|[HTML]c2cfeeMemU     2c|[HTML]c2cfeeNetM     2c|[HTML]c2cfeeInteg     2c|[HTML]c2cfeeUtil

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[HTML]c2cfeeAuto-GPT [77]     [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0

[HTML]c2cfeeBabyAGI [51]     [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0

[HTML]c2cfeeSuperAGI [79]     [HTML]E5E5E52   0    [HTML]E5E5E51   0    [HTML]E5E5E51   1    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E51   1    [HTML]E5E5E52   1    [HTML]E5E5E50   1    [HTML]E5E5E50   0    [HTML]E5E5E50   1    [HTML]E5E5E52   1

[HTML]c2cfeeHuggingGPT [70]     [HTML]E5E5E52   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0    [HTML]E5E5E52   0    [HTML]E5E5E52   0    [HTML]E5E5E52   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0    [HTML]E5E5E52   0

[HTML]c2cfeeMetaGPT [28]     [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0    [HTML]E5E5E51   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E52   0

[HTML]c2cfeeCAMEL [41]     [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E51   0    [HTML]E5E5E50   1    [HTML]E5E5E51   1    [HTML]E5E5E50   0    [HTML]E5E5E50   1    [HTML]E5E5E50   0    [HTML]E5E5E50   0

[HTML]c2cfeeAgentGPT [71]     [HTML]E5E5E52   1    [HTML]E5E5E51   0    [HTML]E5E5E51   0    [HTML]E5E5E50   0    [HTML]E5E5E51   0    [HTML]E5E5E52   0    [HTML]E5E5E51   1    [HTML]E5E5E52   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E52   1

[HTML]c2cfeeZapier* [62]     [HTML]E5E5E51   1    [HTML]E5E5E50   1    [HTML]E5E5E50   1    [HTML]E5E5E50   0    [HTML]E5E5E50   1    [HTML]E5E5E50   1    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   0    [HTML]E5E5E50   1    [HTML]E5E5E50   1

Assessment of autonomy () and alignment () levels across viewpoint-specific aspects of selected LLM-powered multi-agent systems. Detailed level criteria for viewpoint-specific aspects are discussed in Section <ref>. * Zapier, a workflow-automation tool, has been included to contrast the results.

§.§ Taxonomic Classification

The taxonomic classification relies on a detailed assessment of autonomy and alignment levels for viewpoint-specific aspects of the systems. Table <ref> reports on the results of assessing these levels of autonomy () and alignment () for aspects characterizing the four architectural viewpoints applied by our taxonomy. In particular, for , the aspects of decomposition (), orchestration (), and synthesis (); for , the aspects of communication-protocol management (), prompt engineering (), and action management (); for , the aspects of agent generation (), role definition (), memory usage (), and network management (); for , the aspects of resource integration (), and resource utilization  are distinguished. An overview of these viewpoint-specific aspects and corresponding level criteria applied for this assessment is provided in Section <ref>.

* In order to contrast the classification results, we included Zapier [62] into our comparison, a renowned tool offering the automation of workflows based on user-specified tasks.

Radar charts illustrating the system profiles based on an assessment of architectural aspects in terms of autonomy (blue graph) and alignment (green dashed graph) levels. Detailed assessment data can be found in Table <ref>.

Fig. <ref> displays the derived autonomy and alignment levels per multi-agent system using radar (or spider) charts [80]. In particular, architectural aspects form the multiple axes. The level scheme (, , ) for autonomy and alignment is depicted by grey circles linking these axes. The blue graph then represents the assessed autonomy levels, the green dashed graph the corresponding alignment levels.

In what follows, we outline key results from the taxonomic assessment for each system.

* Auto-GPT [77] - enables users to specify multiple goals, which are autonomously decomposed into tasks and then prioritized (L2 autonomy for ); also see Fig. <ref> (a). The system encompasses three distinct task-management agents: an execution agent, a task-creation agent, and a task-prioritization agent. All tasks are actually performed by this singular execution agent sequentially determined by prioritization (L0 autonomy for ). Following the completion of each task, the agent evaluates the intermediate results, engaging in self-criticism. The tasks are optionally re-prioritized. The final result represents an aggregate of all partial results, complemented with a succinct summary (L1 autonomy ). The communication between the three agents follows a predefined communication protocol.  is adaptive based on templates (L1 autonomy). The management of the performed actions is self-organized (L2 autonomy). Agents in the system are pre-configured and instantiated once, showing L0 autonomy. The  of the execution agent is adaptive (L1 autonomy). Both  and  adhere to predefined rules, marking L0 autonomy. Auto-GPT is equipped with a suite of predefined contextual resources (L0 autonomy), which are utilized in a self-organizing manner due to the needs of the scenario, demonstrating L2 autonomy. Across the aspects, the system provides a low level of user interaction. Beyond the transmission of goals, further user interaction is only available in terms of authorizing the subsequent execution step, which however, can also be skipped via the continuous mode, leaving users with no further intervention capabilities.

* BabyAGI [51] - provides very similar functionalities and architectural characteristics to those demonstrated by Auto-GPT. This similarity can be visually observed in the radar charts illustrated in Figs. <ref> (a) and (b). In particular, both systems maintain a high-autonomy level regarding goal , the management of single actions performed by the task-execution agent, and the  of integrated contextual resources due to the requirements of the given tasks. Furthermore, both systems provide transparency by reporting on the execution agent's plans and thinking operations as basis for executing the tasks. They both maintain iterative, but fixed communication protocols allowing for the task-management agents to organize the decomposition and aspects of result synthesis. BabyAGI extends the set of task-management agents by a context agent, which is responsible for context-interaction tasks. In addition, it also allows the user to configure a few constraints governing the use of the LLM.

* SuperAGI [79] - also allows users to specify a goals or complex task, which are decomposed autonomously into tasks tackled sequentially by an LLM-powered agents. Thereby, its functionalities and architectural characteristics are in some regards similar to Auto-GPT and BabyAGI (see above). For instance, the executing agent is performing the assigned tasks autonomously, showcasing L2 autonomy in ; also see Fig. <ref> (c). The systems leverages predefined, but adaptable prompt templates (L1 autonomy for ). Contextual resources are also utilized in an autonomous manner, indicative of L2 autonomy.
Diverging from its counterparts, SuperAGI necessitates that users create a dedicated agent for every distinct goal. In contrast to the other two systems, the roles of these agents are highly task-adaptive (L2 autonomy) and can be influenced by user, attributing to L1 alignment. Moreover, the  of tasks is more adaptive (L1 autonomy).
A distinctive feature of SuperAGI is its ability to incorporate various alignment strategies. For instance, it permits constraints regarding , allowing users to cap the context window's length, a trait of L1 alignment. In terms of , SuperAGI comes with an array of pre-configured tools, all of which can be authorized for . Furthermore, data can be uploaded, empowering agents to seamlessly incorporate and utilize it (L1 alignment). Though SuperAGI can handle multiple goals and agents, these agents work in parallel, separately. There is no actual collaboration between the agents, and no further configuration options for the user, resulting in L0 autonomy and alignment for  and .

* HuggingGPT [70] - follows a different strategy by leveraging the LLM as an autonomous controller that combines various multi-modal AI models to solve complex tasks. In this, it integrates with the Hugging Face platform that provides a large pool of foundation models available for utilization. This singular central LLM-powered agent, tailored to solve the given goal, is autonomous in breaking down the goal or complex task into manageable tasks (L2 autonomy for ) as well as in selecting, combining, and applying the appropriate models via prompting, achieving L2 autonomy for  and  of contextual resources as well as for , , , . However, not every aspect of HuggingGPT exhibits such high autonomy. Some procedural aspects are predefined, but adaptive to the given task, such the high-level process framework consisting of the predefined phases of planning, model selection, task execution, and response generation. Despite its autonomy in many aspects, HuggingGPT does not grant users any further degree of user customization, resulting in L0 alignment for all aspects; see Fig. <ref> (d). In sum, based on our autonomy-alignment matrix (see Table <ref>), the system shows tendency towards  (#7; L2 autonomy and L0 alignment).

* MetaGPT [28] - aims to solve complex programming tasks (specifically in Python) by leveraging the synergies of multiple collaborating LLM-powered role agents. Thereby, the framework simulates human workflows and responsibilities inherent to software-development project.
For this purpose, the task-management activity comprises distinct phases similar to the waterfall process (such as RE, design, coding, testing), each with dedicated role agents responsible for autonomously executing the associated tasks. Each phase delivers certain artefacts then processed by the next phase (e.g., design specification). In particular, the user-specified requirements are autonomously transferred into these different artefacts (L2 autonomy for ), which are finally also combined to product a tested software program, achieving L2 autonomy for ; also see Fig. <ref> (e). As mentioned aove, the actual  follows a defined scheme, termed as standardized operation process, resulting in L0 autonomy. Both  and  are predefined (L0 autonomy). Within their designated phases, the agents display pronounced autonomy, exhibiting adaptability in their  corresponding to the specificity of tasks, thereby reaching L2 autonomy.  is predefined, but adapted for inter-agent collaboration (L1 autonomy). Contextual resources are autonomously utilized as needed, marking L2 autonomy for . Similar to HuggingGPT, MetaGPT showcases low levels of alignment for the user (L0), since it provides no further configuration or adjustment options for the user.

* CAMEL [41] - aims to explore the potentials of autonomous cooperation among communicative LLM-powered agents to accomplish complex tasks. Similar the most other multi-agent systems, it aspires to handle given user-prompted goals autonomously. To this end, a dedicated generic task-specifier agent breaks down the goal into a list of manageable tasks (L2 autonomy for ), also see Fig. <ref> (f). Subsequently, these tasks are processed by a pair of agents working in tandem through a cyclical dialogue pattern, wherein the AI-user agent lays out the directives, and the AI-assistant agent assumes the role of the executor. This strict modus operandi corresponds to L0 autonomy in , , and . The specific  of these predefined agent archetypes can be selected by the user (L1 alignment). Augmenting this duo are other specialized agents designed for specific roles, including task allocation and strategic planning. In contrast to most other analyzed systems (except MetaGPT), CAMEL provides actual collaboration between role agents executing the given tasks. During the task-execution phases, agents operate with a marked sense of autonomy, achieving L2 in both  and . Alignment options for the user are provided via the definition of agents, encompassing their  and interrelation in the , resulting in L1 alignment.

* AgentGPT [71] - also strives to accomplish a user-prompted goal by leveraging a single task-execution agent, who can be created by the user by specifying its goal, resulting in L1 alignment for .  The agent systematically addresses tasks, prioritizing them based on a predefined list and capitalizing on contextual resources, all in a self-organized manner corresponding to L2 autonomy.
In terms of autonomy levels across different facets, it closely mirrors SuperAGI, as can be observed in Fig. <ref> (c) and (g). However, when it comes to alignment possibilities, AgentGPT diverges slightly. On the one hand, it provides no adjustment options regarding the agent's role or the  of results (both L0 alignment). On the other hand, it introduces the option to extend the task list by inserting custom tasks, achieving L1 alignment for .

* Zapier [62] - Unlike the other entities discussed, Zapier focuses on workflow automation based on user-specified tasks and does not represent an LLM-powered multi-agent system. Its inclusion in this classification serves to contrast the results, providing a clearer understanding of the capabilities and potential limits of LLM-powered systems when juxtaposed with traditional task-oriented automation platforms. In particular, in Zapier, users need to define step-by-step instructions to facilitate the automation process. The resulting workflows can work in parallel, but lack the capability for direct inter-task interactions.
Zapier offers configuration options (pre-runtime) for diverse aspects related to  and , resulting L1 alignment; also see Fig. <ref> (h). Given its non-reliance on LLM-powered agents, it naturally secures an L0 ranking in both autonomy and alignment for agent-centric attributes. Nevertheless, it leverages LLMs to process textual tasks such as writing emails, or for decomposing user-specified goals into tasks, a feature users can optionally activate (thus L1 autonomy for ). Drawing from our autonomy-alignment matrix, detailed in Table <ref>, Zapier is aptly categorized as  (#2; signifying L0 autonomy and L1 alignment).

Given its unique positioning as a workflow automation system, Zapier provides an illustrative deviation from these trends. Its strategic approach is distinct, predominantly showcasing lower levels of autonomy, as the LLMs are only leveraged for specific, limited tasks. Conversely, it favors a strategy of extensive user-guided alignment, applicable to all except the agent-specific aspects. Note, however, that alignment here is not applied as an enhancement or refinement to actually align the system's operation to the user's goal or intention, but in terms of specifications of process steps with detailed instructions essential for the system's operation.

§.§ Comparative Analysis

In the following, we discuss the distribution of assessed levels (Section <ref>) and explore strategies across system categories (Section <ref>).

§.§.§ Comparison of Assessed Levels

Fig. <ref> offers an overview of how the assessed levels of autonomy and alignment distribute over the 12 categories of architectural aspects of the seven selected multi-agent systems. Detailed assessment data is provided in Table <ref>.

Distribution of identified autonomy and alignment levels across architectural aspects of selected LLM-powered multi-agent systems, represented as stacked bar charts with corresponding data provided below.

On the one hand, three groups of aspect categories emerge when assessing autonomy levels, each displaying a certain degree of homogeneity. A detailed representation can be found in Fig. <ref> (a).

* High-Autonomy Aspects: Among the systems, we encounter a high-autonomy strategy for certain aspects, demonstrated by self-organizing and autonomously deciding LLM-powered agents. This strategy is particularly evident for the decomposition of goals into manageable tasks (), for the management of actions, encompassing the actual performance of different task-related actions (), as well as for utilizing the contextual resources such as tools and data (). Nearly all systems delegate the responsibilities for these aspects to the LLM-powered agents, which corresponds to  autonomy.

* Medium-Autonomy Aspects: For other aspects, systems lean towards a semi-autonomous strategy (), featuring predefined mechanisms adaptable by the LLM-powered agents. This is prominently observed in two aspects. First, in result synthesis (), by combining the task results guided by a predefined framework adaptable by the LLM-powered agents. Second, in the engineering of prompts (), such during prompt augmentation by adapting predefined prompt templates.

* Low-Autonomy Aspects: Several architectural aspects showcase a deterministic strategy with rule-based mechanisms and automation, demonstrating  autonomy, which can observed for the following aspects:

* orchestrating and distributing the tasks ().

* guiding the collaboration between the agents ().

* managing the utilization of memory, such as for reflecting and planning ().

* managing the agent network, such as regarding the relationships between the agents ().

* integrating contextual resources ().

Variable-Autonomy Aspects:
The autonomy levels for the aspects of agent generation () and role definition () display notable variability, as depicted in Fig. <ref> (a). This heterogeneity is reflective of the different strategies employed by the multi-agent systems under analysis (detailed in Section <ref>).

Integrated and User-Guided Alignment:
Drawing insights from Fig. <ref> (b), it emerges that the predominant strategy across most systems is to maintain lower levels of alignment across all assessed aspects. This primarily manifests in alignment techniques already integrated into the system architecture (L0 alignment), offering little to no options for user adjustment. Furthermore, low-autonomy aspects with predefined and automated mechanisms can be used to control and align other higher-autonomy levels. Thus, these mechanisms can be seen as manifestations of integrated alignment.
However, we observe a noticeable inclination for systems to provide user-guided alignment (L1) for specific aspect categories, namely the agent generation (), agent role definition (), and contextual resource utilization (). Furthermore, the data reveals a consistent lack of real-time responsive alignment options across all examined systems. Nonetheless, in this context, it is worth mentioning that some systems at least facilitate monitoring functionalities available for system users (often termed as verbose mode), which provide transparency of the reasoning and decision-making performed by the execution agents during task reflection and planning. This transparency grants users the leverage to either greenlight or halt the impending actions. However, there are no possibilities to further influence the task planning or execution, such as by adjusting or refining task planning.

Intertwined Dependencies:
As evident from the radar charts in Fig. <ref>, a diverse range of autonomy levels manifests both within and across architectural viewpoints of the analyzed systems. This variance results in a complex web of intertwined dependencies between the aspects:
Certain aspects have to deal with diverse dependencies. While dependent on predefined mechanisms or resources provided by low-autonomy aspects (availability-driven dependencies), they have to adapt dynamically in response to situational imperatives set by other high-autonomy aspects (requirements-driven dependencies).
This complexity resulting from intertwined dependencies can be seen as challenging for ensuring accurate process execution. A detailed description of these challenges associated with architectural dependencies is provided in Section <ref>.

§.§.§ Strategies Across System Groups

We now explore how different categories of systems balance the interplay between autonomy and alignment.
Based on our taxonomic classification and the resulting system profiles as illustrated in Fig. <ref>, we can categorize the selected 7 systems under analysis into three distinct system groups, which encompass general-purpose systems, central-controller systems, and role-agent systems.
It's important to note that our categorization into these three groups, based on the systems chosen for this exploration, doesn't capture the entire spectrum of autonomous LLM-powered multi-agent systems. For a comprehensive overview of existing systems and system categories, we recommend referring to the recent surveys provided by [84, 95]. In the following, the key characteristics as observed from the corresponding system profiles are discussed.

* General-Purpose Systems - representing multi-agent systems designed for and adaptable to a broad spectrum of tasks and applications. Within the analyzed set of multi-agent systems, the following fall into this group: Auto-GPT [77], BabyAGI [51], SuperAGI [79], and AgentGPT [71]. Goals are decomposed autonomously and represented as prioritized task lists (L2 ). They employ a multi-cycle process framework performed by dedicated task-management agents represented by certain generic agent types, including a single task-execution agent (see Section <ref>). Relations and communications between these agents are strictly predefined, and agent conversations express as a monologue of the task-execution agent, resulting in low autonomy levels (L0) for communication protocol (), and network management (). The task-related actions are performed autonomously by the task-execution agent (mostly L2 autonomy ). while resource integration is based on provided mechanisms (), the resources are selected and utilized by the LLM-powered in a self-organizing manner (L2 autonomy for ), except for CAMEL; resulting in similar autonomy profiles for the aforementioned aspects.
Besides from these commonalities, these systems distinguish in certain characteristics. Both Auto-GPT and BabyAGI employ generic task-execution agent, and provide no further alignment options at all. Moreover, these systems employ a generic task-execution agent with predefined agent roles and relations, reulting in L0 autonomy for  and .
In contrast, SuperAGI and AgentGPT employ execution agents with self-organizing agent roles (L2 autonomy for ), an adaptable orchestration process (L1 for ), and some alignment options, especially for agent-specific aspects.
Moreover, these systems employ execution agents, whose roles can be customized by the user (L1 alignment for ).

* Central LLM Controller - marks a third group specialized in leveraging and combining contextual resources for accomplishing the complex goals. HuggingGPT [70] serves as an archetype of such systems, utilizing resources especially in terms of existing ML models integrated via Hugging Face.
As already detailed in Section <ref>, HuggingGPT is characterized by a single central LLM-powered control agent with monologue-based reflection and planning. Language in terms of agent prompts as generic interface to manage the interplay between multiple specialized foundation models. In comparison to other systems or system groups, we see the highest levels of autonomy granted to this central agent (mostly L2); also see Fig. <ref> (d).
Furthermore, we see a finite and artefact-oriented process adaptable by the LLM-powered agent for orchestrating the different model-related tasks (L1 autonomy). As already stated above, beyond prompting the task, there are no further user-centric alignment options (L0 alignment).

* Role-Agent Systems - employ an interplay or simulation between multiple dedicated roles agents. This collaboration can serve different purposes, such as simulating a discussion or solving tasks that demand for a multi-perspective collaboration. With defined roles in a certain environment (such as in a software development project), their application is bound to this application domain or special purpose. Among the analyzed systems, MetaGPT [28] and CAMEL [41] represent such systems. In contrast to the general-purpose systems, the execution agents play roles with dedicated responsibilities in a certain application domain. Furthermore, these role agents actually collaborate directly with each other. In case of the two exemplary systems, this collaboration is realized by communication protocols employing a dynamic exchange between agents with instructor and executor roles.
In particular, CAMEL employs two such role agents based on predefined agent types, but adjustable by the user. In ongoing strict dialogue cycles, the AI-user role agents instructs the AI-assistant role agent to execute the tasks (L0 autonomy for ). Similar to SuperAGI, CAMEL requires the user to specify the agents' roles (L1 alignment). MetaGPT, in contrast, internally assigns predefined roles with responsibilities alongside a waterfall development process (L0 alignment); thus, also expressing a finite and artefact-oriented process (L0 autonomy for ), terminating with the produced and tested software program. However, like in real-world software project, refinement iterations can follow, optional feedback cycles make it adaptable for the agents (L1 autonomy for ).

Strategy Assessment. Beyond differences in the applied communication protocols, it is the flexibility of agent roles (in relation to both autonomy and alignment) and further customization options for agent-specific aspects that distinguishes the systems' strategies (see above).
However, when examining how the systems deal with autonomy and alignment across further aspects, most systems and system groups show similar strategies.

The reasoning capabilities of LLM-powered agents are especially leveraged in areas demanding high autonomy, such as the goal decomposition, the actual execution of task-related actions, and the utilization of contextual resources. Interestingly, these high-autonomy aspects are mostly combined with low alignment levels, resulting in bounded autonomy aspects (refer to Table <ref>).
A closer look at aspect interdependencies, as depicted in Fig. <ref>, reveals that these internally unbalanced aspects are accompanied by other low-autonomy aspects equipped with limited flexibility, as follows:

* Autonomous decomposition directly depends on the user-prompted goal.