A multi-agent system is an AI architecture in which multiple independent AI agents collaborate to complete a task. Each agent has a defined role, access to specific tools or data sources, and the ability to pass results to other agents. The agents are coordinated by an orchestration layer that manages the flow of work between them.

The term “agent” in this context means an AI component that can take actions - call tools, query databases, invoke APIs - and make decisions about what to do next based on the results. A single LLM call that takes input and returns output is not an agent. An AI system that can autonomously execute a multi-step workflow, handling branches and errors along the way, is.

Architecture Patterns

Orchestrator-worker - The most common pattern. A central orchestrator agent receives the task, decomposes it into subtasks, and delegates each subtask to a specialized worker agent. The orchestrator collects worker outputs and synthesizes the final result. Easy to reason about and debug because the orchestrator maintains a clear view of the full task state.

Example: An orchestrator receives a research request, delegates to a web search agent, a database query agent, and a document summarization agent, then synthesizes their outputs into a final report.

Pipeline - Agents are arranged in a sequence. The output of Agent 1 becomes the input to Agent 2, and so on. There is no central coordinator. Suitable for linear workflows with clearly defined stage boundaries.

Example: A document processing pipeline where a classification agent categorizes the document, an extraction agent pulls structured fields, a validation agent checks the output, and a routing agent sends it to the right destination.

Hierarchical - Multi-level orchestration. A top-level orchestrator delegates to sub-orchestrators who manage groups of workers. Used for very large workflows with many agents where a single orchestrator would have too many responsibilities to manage coherently.

Debate and consensus - Multiple agents receive the same task independently and produce separate outputs. A judge agent (or human reviewer) evaluates the outputs and selects or synthesizes the best result. Used when output quality is critical and independent validation improves accuracy.

When Single-Agent Is Not Enough

A single agent with access to multiple tools handles a large proportion of real-world use cases. Consider moving to multi-agent when:

  • Context limit pressure - A single workflow generates more context than one model can process accurately. Multi-agent systems pass only relevant summaries between agents rather than accumulating the full history.
  • Specialized expertise - Different parts of the workflow require genuinely different prompting strategies or knowledge. A general-purpose agent performing medical document extraction plus legal contract review performs worse than two specialized agents.
  • Parallel processing - Multiple independent subtasks can complete simultaneously. A single agent processes them sequentially; a multi-agent system parallelizes them.
  • Quality requirements - Independent validation by a second agent catches errors the first agent misses. This is particularly valuable in high-stakes workflows.

Frameworks

LangGraph - Graph-based agent orchestration. Highly flexible; supports cycles (an agent can loop until a condition is met) and conditional branching. Best for complex workflows with non-linear execution paths.

CrewAI - Role-based framework with a natural language interface for defining agent roles and tasks. Lower implementation overhead than LangGraph. Good for collaborative workflows where human-readable role definitions matter.

AWS AgentCore - AWS’s managed agent runtime, integrated with Bedrock. Handles memory, session management, and tool routing as managed infrastructure. Best for production workloads on AWS where operational reliability and managed scaling matter.

AutoGen - Microsoft’s conversation-based framework. Agents communicate through a structured chat interface. Strong tooling for code execution workflows.

Observability

Multi-agent systems are harder to debug than single-model calls because errors can originate at any point in the agent chain and compound through subsequent steps. Every agent action, tool call result, and inter-agent message should be logged with a shared trace ID. When output quality issues appear, the trace is the primary diagnostic tool.

Sources and Further Reading