Most AI use cases can be handled by a single model call with a well-constructed prompt. But as workflows grow in complexity - involving multiple tools, conditional logic, long chains of reasoning, or specialized domain tasks - single-model architectures start to show limits. Multi-agent systems address this by coordinating multiple AI models, each focused on a specific part of the problem.

What a Multi-Agent System Is

A multi-agent system is an architecture where multiple AI agents collaborate to complete a task. Each agent typically has:

  • A specific role or capability (researcher, writer, validator, code executor)
  • Access to a defined set of tools (web search, database query, API call)
  • A way to pass outputs to the next agent in the workflow

The coordination layer - which agent runs next, how outputs are passed, how errors are handled - is what distinguishes the different frameworks and architecture patterns.

When You Need Multi-Agent Architecture

Complex workflows with distinct stages - If a task involves research, then analysis, then report writing, each stage has different tool requirements and quality criteria. A single agent trying to do all three tends to lose focus and context.

Specialized domain tasks - A financial document processor has different requirements than a legal contract reviewer. Separate agents with specialized prompts and tool access outperform a single generalist agent.

Parallel processing - When multiple independent subtasks need to complete before an aggregation step, parallel agent execution reduces latency significantly. A research task that queries five data sources can run all five queries simultaneously rather than sequentially.

Human-in-the-loop workflows - Multi-agent systems make it natural to insert human review checkpoints. An agent produces a draft, a human approves or rejects, the next agent continues only after approval.

Long-running processes - Single LLM calls have context length limits. Multi-agent systems can break long processes into steps, summarizing and passing only relevant context forward.

Frameworks

LangGraph - Graph-based agent orchestration from LangChain. Nodes are agents or tools, edges are transitions. Supports conditional branching and cycles, which makes it appropriate for workflows where the path is not known in advance. Strong Python ecosystem, good observability tooling.

CrewAI - Role-based multi-agent framework where you define a “crew” of agents with human-readable roles and a shared goal. Lower implementation overhead than LangGraph for straightforward collaborative workflows. Good starting point for teams new to multi-agent systems.

Amazon Bedrock AgentCore - Amazon’s managed runtime for AI agents, and AWS’s recommended path for new agent builds now that the older Bedrock Agents (“Bedrock Agents Classic”) closed to new customers on 30 July 2026. Handles the infrastructure concerns (runtime, memory, session management, tool gateway, identity) so you can focus on agent logic. Works with agents built in any framework, including LangGraph and CrewAI, and integrates with Bedrock models and AWS services. Suitable for production workloads where operational reliability matters. See Bedrock AgentCore .

Microsoft Agent Framework - Microsoft’s production multi-agent framework (1.0, with stable APIs), which succeeds both AutoGen and Semantic Kernel and supports multiple model providers plus A2A and MCP. AutoGen, its conversation-based predecessor where agents communicate through structured chat, is now in maintenance mode: it receives no new features, and Microsoft directs new users to Agent Framework. See Microsoft Agent Framework .

Architecture Patterns

Orchestrator-worker - A central orchestrator agent decomposes a task and delegates to specialist worker agents. The orchestrator aggregates results. Simple to reason about, easy to debug.

Pipeline - Agents are arranged in a sequence. Output of agent N is input to agent N+1. No central coordinator. Works well for linear workflows with clear stage boundaries.

Hierarchical - Multi-level orchestration. A top-level orchestrator delegates to mid-level coordinators who manage worker agents. Used for very complex workflows with many agents.

Practical Considerations

Start with the simplest architecture that solves the problem. A two-agent system (one to retrieve, one to synthesize) is dramatically simpler to debug than a six-agent system. Add agents only when a specific limitation - quality, latency, context length - makes a single agent clearly insufficient. Most enterprise AI use cases run effectively with two to four agents.

Observability is not optional. Every agent action, tool call, and handoff should be logged. When a multi-agent system produces an incorrect output, you need a trace to understand where the reasoning went wrong.

Sources

  1. Microsoft, AutoGen README (maintenance-mode notice; fetched 25 September 2026): https://github.com/microsoft/autogen
  2. Microsoft, Agent Framework repository: https://github.com/microsoft/agent-framework
  3. AWS Documentation, “Amazon Bedrock Agents Classic maintenance mode”: https://docs.aws.amazon.com/bedrock/latest/userguide/agents-classic-maintenance-mode.html