Distribute work across specialized AI agents—each with distinct roles, tools, and memory—to solve complex, multi-step problems that overwhelm single models. Learn how agents communicate, coordinate through orchestrators, and leverage frameworks like CrewAI and LangGraph to build collaborative systems.
For most of AI's recent history, the dominant architecture was simple: one model, one conversation, one task at a time. A user asks a question, a language model responds. This works remarkably well for a wide range of problems — but it breaks down when tasks become long, complex, multi-step, or require kinds of expertise that no single model handles equally well. Multi-agent systems (MAS) are an architectural response to exactly this limitation.
A multi-agent system is a network of individual AI agents, each with its own role, tools, memory, and reasoning process, that collaborate to accomplish a shared objective. Rather than one generalist model doing everything, work is distributed across specialists. The result is a system that can tackle problems of a scale and complexity that would overwhelm any single agent.
Think about how a professional team operates. A law firm doesn't have one person who simultaneously researches case law, writes briefs, manages client relationships, and handles billing. Roles are divided because specialization improves quality and because parallel work saves time. Multi-agent systems encode this same organizational logic into AI architecture.
Single models face concrete limitations:
Multi-agent systems address all four of these limitations by design.
Every multi-agent system is built from a small set of foundational components. Understanding these pieces — and how they fit together — gives you the mental model to reason about any MAS you encounter or design.
An agent is an autonomous entity that perceives inputs, reasons about them, and takes actions. Each agent in a MAS has a defined role (what it does), tools (what capabilities it can invoke), and memory (what context it retains across steps). Agents may be powered by large language models, but they are more than just prompt-response cycles — they operate in loops, making decisions based on feedback from their environment.
Each agent is assigned a narrow, specific purpose. One agent might be a researcher whose only job is to retrieve and summarize information. Another might be a critic whose job is to find flaws in a draft. A third might be an executor that calls APIs or writes code. This specialization is what allows each agent to be optimized for its task rather than compromised by trying to do everything.
Agents must pass work to one another. This happens through message passing — structured outputs from one agent become structured inputs for the next. The design of these handoffs is critical: poorly structured communication leads to agents working from incomplete or misformatted context. Frameworks like LangGraph and CrewAI provide explicit mechanisms for defining how agents hand off work.
Someone has to decide which agent runs when. This is the orchestrator — sometimes a dedicated agent, sometimes a framework-level construct. The orchestrator routes tasks, resolves dependencies between agents, and determines whether the workflow is sequential (agents run one after another) or parallel (agents run simultaneously on different sub-problems).
Agents can have different scopes of memory: short-term memory (the current context window), shared memory (a common store accessible to multiple agents), and long-term memory (persistent storage retrieved across sessions). Memory architecture determines whether agents can build on each other's work or must start from scratch each time.
Agents become far more capable when given access to tools — web search, code execution, database queries, API calls. Tools are the mechanism by which agents act on the world rather than merely reasoning about it.
A typical multi-agent architecture: an orchestrator routes tasks to specialised agents (researcher, reasoner, executor, critic), all of which read from and write to shared memory.
Two frameworks you'll work with throughout this domain make MAS practical to build.
CrewAI organises agents into crews — defined collections of agents with assigned roles, each equipped with tools, that collaborate on a task. CrewAI abstracts the communication protocol, letting you declare what each agent does and how they interact rather than hand-coding message passing.
LangGraph takes a graph-based approach. Agents are nodes; the edges between them define how state and messages flow through the system. LangGraph is particularly well-suited to workflows with conditional branching — for example, routing to a critic only if the output quality falls below a threshold.
Both frameworks let you define agent pipelines declaratively, but they differ in how explicit the control flow is. LangGraph makes the graph structure visible and inspectable; CrewAI focuses on role-based delegation. Understanding both gives you two complementary tools for architecting multi-agent solutions.
Not every problem benefits from multiple agents. For simple, bounded tasks — summarise this paragraph, classify this sentiment — a single model is faster, cheaper, and less complex to maintain. Multi-agent systems earn their overhead when:
Developing the intuition to judge when to reach for a multi-agent architecture — and when to keep things simple — is one of the most practical skills this domain will build.
The concepts that follow build on this overview systematically. You'll start with what an agent is at a precise level, then learn how agents specialize, how they communicate, how memory and tools work, how orchestration strategies differ, and how to apply all of this to real workflows. By the end, you'll have both the conceptual vocabulary and the practical frameworks to design collaborative AI systems from scratch.