1 - A look back at the evolution towards Multi-Agents
Today, we continue our exploration of the world of generative AI, going one step further in our understanding of autonomous agents. In the previous articlewe saw how AI evolved from basic classification and regression algorithms, to deep learning, then Transformers, to advanced conversational tools like ChatGPT. These advances have put in our hands assistants capable of responding, learning, and even endowing themselves with a certain form of "internal logic".
But there's a new leap ahead: multi-agent systems. We're no longer talking about a single agent, but entire teams of AIs working hand in hand. The idea is simple: faced with growing complexity, why not divide the work between several specialized agents, each with its own role, tools and expertise? This concept transforms the way we interact with AI, paving the way for a new era of autonomous collaboration.
The idea is a powerful one: rather than executing commands on an ad hoc basis, AI agents operate as "digital foremen", coordinating different actions and tools, adapting to a variety of contexts. In this way, AI ceases to be a mere tool and becomes a genuine collaborator, a force for proposals and innovation.
2. The limits of the Single Agent
Initially, one of the great benefits of LLMs was that they were excellent "monolithic" assistants: a single model to answer all kinds of questions. This configuration is suitable for simple tasks, but quickly reaches its limits as complexity increases. A single agent can get lost in the multitude of tools at its disposal, struggling to manage an immense context, and lacking the specialization to deal with cutting-edge challenges.
To understand how these agents gain in autonomy, we must delve into the heart of their architecture, often described by the triptych "Perception - Reflection - Action":
- Perception : The agent is not limited to text. It can interpret visual, audio, geospatial or specialized API data. This capability considerably broadens its scope, enabling it to understand a much richer context than a simple text prompt.
- Reflection/Brain : The heart of the agent is an LLM, endowed with memory, knowledge, reasoning and planning capabilities. This "brain" determines the steps to be taken to reach the set objective, reacts to unforeseen events and improves with each interaction. It can also manage several tasks simultaneously, prioritize, learn new strategies and demonstrate creativity.
- Action: Once the plan has been defined, the agent takes action. It can write text, call an API to extract or push data, run scripts, manipulate objects in a virtual environment, even interface with physical tools. Action is what differentiates an AI agent from a simple model responding passively to requests. Here, the agent actually "does" something, interacts with the environment and influences the course of events.
These principles apply equally to individual agents and to multi-agent systems, where each entity has its own perception, its own brain and its own capacity for action. The difference lies in coordination: in a multi-agent system, communication mechanisms (memory sharing, message exchange, centralized supervision) enable agents to collaborate, learn from each other, or even simulate a kind of artificial "society". This notion of a "society of agents" - a set of intelligent entities interacting with each other - opens the way to complex scenarios where agents help each other, negotiate, specialize and work together to achieve common goals.
The move from a single agent to groups of agents is not just a fad, it's a necessity. A single agent, no matter how efficient, comes up against several obstacles:
- Tool overload : A single agent connected to a multitude of tools can quickly get lost in choosing and selecting the best resource at the right time.
- Limited context : The more tasks are linked together, the longer the history, and the more the agent's ability to retain and process information efficiently diminishes.
- Lack of Specialization: The same agent must in turn plan a project, analyze financial data, write an article, even code a script... It's hard to excel simultaneously in so many areas without losing efficiency.
The single agent is therefore often overwhelmed: its capabilities, though powerful, have limits that become apparent as complexity increases.
3. Towards a Multi-Agent approach: Collaboration, Hierarchy and Communication
To overcome these limitations, the multi-agent approach involves the cooperation of several autonomous agents, each with a clearly defined role:
- Specialization by Role : Instead of a "jack-of-all-trades" agent, we create expert agents - an "analyst agent" for data, a "creative agent" for content generation, a "planner agent" for structuring tasks, etc. - to help you make the most of your resources.
- Central Supervisor : An agent overseeing the whole system can direct the flow of information, decide who to entrust with the next mission, and ensure overall consistency.
- Structured communication : Agents communicate via a shared state, dedicated tools, or message exchanges. This fluid interconnection guarantees a better distribution of responsibilities and a reduction in the cognitive load on each individual agent.
In short, the multi-agent approach makes it possible to distribute roles and create AI teams where each member knows his or her mission. The result? An AI that is more robust, more reactive, and better equipped to respond to the complex challenges facing businesses today.
4. Emerging frameworks for orchestrating agents
To effectively orchestrate a team of autonomous agents, it's not enough to create them and assign them roles. You also need frameworks capable of managing the entire communication chain, task distribution, context persistence and integration with external resources. These tools, still in their infancy on the market, are evolving rapidly and paving the way for new organizational models. Here are a few particularly representative examples of how these tools are helping to structure work between agents.
LangGraph: a Graph-based approach to flow clarity
LangGraph stands out for its highly structured vision: the logic of the multi-agent orchestra is conceived as a graph. Agents (and their tools) are represented by nodes, while information flows and tasks form the links between them. This visual, modular approach makes it easy to understand "who does what" and "how". The result? Clearer control of data flow, the ability to manage complex feedback loops, and better readability for adjusting the system over time. LangGraph is particularly well suited to scenarios where traceability, modularity and scalability are key concerns.
AutoGen: a tool for software development and complex orchestration
AutoGen, developed by Microsoft, was one of the first frameworks to explore multi-agent orchestration. Originally designed to automate software development tasks, AutoGen coordinates interactions between a User Agent and an Assistant Agent within the same ecosystem. Its aim is to transform a request, often focused on code generation or execution, into a sequence of fluid actions.
Key assets :
- Software development orientation : Where other frameworks focus on content generation or search, AutoGen excels in code production, analysis and script execution. It manages iterative loops, where the assistant agent generates code, executes it, and returns the results to the user agent, guaranteeing a complete workflow.
- Solidity and Community: Backed by Microsoft, AutoGen benefits from solid support and technical documentation that can resolve a number of practical problems. This robustness is a considerable asset, especially for teams already familiar with Microsoft tools.
Limits and Complexity :
- Less intuitive for non-developers : AutoGen is particularly well suited to technical profiles. Users with little programming or software orchestration experience may find it more difficult to get to grips with.
- Technically advanced : Integrating AutoGen with local LLMs or deploying it in complex environments sometimes requires a proxy server or advanced configuration, making implementation more demanding than other, more turnkey frameworks.
CrewAI: clear roles, quick launch
CrewAI relies on ease of use and a logic based on well-defined roles. Here, you can quickly set up a "team" of agents, each with an explicit function: research, writing, analysis... CrewAI's main asset is its user-friendliness, enabling you to create a team of agents without necessarily plunging into technical complexity. For a company wishing to rapidly test a prototype multi-agent system - for example, an internal assistant grouping together several "virtual experts" - CrewAI offers an intuitive and straightforward framework. It's an excellent starting point for learning about agent interactions, before moving on to more sophisticated or advanced solutions.
OpenAI Swarm: experimentation and operational simplicity
OpenAI Swarm is an experimental framework designed to lower the barrier to entry for multi-agent systems. It focuses on simplicity and speed of implementation, without trying to cover everything. The idea is to enable experimentation with scenarios involving several agents, to validate hypotheses or test configurations before deployment on a larger scale. At this stage, Swarm is not intended to support complex production environments, but it does provide an ideal playground for understanding multi-agent dynamics and assessing the relevance of this model within an organization.
Common functions, key differences
Despite their differences, these frameworks share a few essential points in common:
- Process Orchestration They provide mechanisms for organizing the sequence of tasks between agents, ensuring that each receives the information it needs, at the right time.
- Context management Whether it's via a graph, a shared state, or a message log, each framework provides a way of preserving history and context, so that agents can work seamlessly, without having to relearn everything at every stage.
- Adaptability to external tools Whether integrating LLMs, in-house tools, APIs or even databases, these frameworks facilitate the connection between agents and external resources, multiplying their capacity for action.
What sets them apart, however, is their maturity, technical complexity and strategic focus. LangGraph stands out for its extensibility and visual representation, CrewAI for its rapid implementation, and Swarm for its experimental lightness. The choice of framework will therefore largely depend on the context: business needs, size of the technical team, degree of specialization required, and desired level of integration with existing systems.
These tools show the direction in which the ecosystem is heading: increasingly structured solutions, capable of managing agents with varied skills, guaranteeing overall consistency, and simplifying the deployment of real AI "teams" at the service of the company.
5. Benefits and limitations: finding the right balance
Beyond the principles, selecting a multi-agent framework often boils down to a game of balances:
- Complexity vs. Simplicity : Some tools, more granular and extensible (such as LangGraph), require a steeper learning curve, while others, more intuitive (CrewAI, for example), enable you to quickly set up a prototype. The choice will depend on the desired degree of orchestration precision and the maturity of the technical team.
- Robustness vs. Experimentation : for operational deployment, stability, technical support and documentation are crucial. A less proven framework, such as OpenAI Swarm, is more suited to exploration and R&D than to immediate integration into a sensitive production pipeline.
- LLM variability and tools : Some frameworks integrate easily with a wide range of models and APIs, while others remain more closed or require more extensive configuration. Adaptability to different LLMs, data lakes, information systems and internal technical environments is a decisive factor.
- Community and Ecosystem : the support of an active community, training resources and shared feedback make it easier to get started and solve practical problems. Before making a choice, examining the quality of the ecosystem and the dynamics around the framework can avoid many long-term stumbling blocks.
6. A case study: the Autonomous Virtual Team
Imagine a company looking to improve its business intelligence and decision support processes. Instead of a single assistant trying to cover all aspects, you could deploy an "AI team":
- Agent Supervisor (Virtual Project Manager) : it receives the request (for example: "Analyze market trends in sector X") and breaks down the task.
- Research agent : querying databases, consulting articles, drawing up a documentary brief.
- Financial Analysis Officer : data processing, scenario simulation, budget estimates.
- Agent Rédaction : final synthesis, report formatting, adaptability of content to internal audience (management, marketing, R&D...).
Thanks to an adapted framework, the supervisor coordinates these roles, avoiding redundancy, managing the context between each stage, and guaranteeing overall consistency. The AI team thus formed reduces cognitive load, accelerates knowledge production, and improves the quality of decision-making.
7. Reflection and outlook
In the background, these multi-agent systems raise the question of technical and ethical governance: how to maintain several specialized entities, how to control costs (in terms of computing resources and configuration time), and how to guarantee the reliability and relevance of responses when agents rely on multiple sources?
Nevertheless, the rise of dedicated frameworks bears witness to the desire to democratize and facilitate the implementation of these AI teams. By naturally integrating distinct agents, providing the means to supervise their interactions and managing operational complexity, these tools are leading us towards a new generation of more collaborative, intelligent and resilient solutions.
The question to ask yourself: in your sector of activity, what processes could benefit from this collaborative logic between specialized machines?
Whether to automate competitive intelligence, refine a communications strategy, accelerate R&D or streamline customer service, multi-agent frameworks offer the promise of a profound reorganization of automated work. As their maturity grows, we'll see the emergence of an ecosystem of systems that are increasingly adaptive, reliable and capable of catalyzing the digital transformation of businesses.
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By Jérémy BRON, AI Director, Silamir Group
