What Is a Multi-Agent System?

A multi-agent system is a team of AI agents that split responsibilities to reach a shared goal. Instead of one super-agent doing everything, specialized agents collaborate—planner, researcher, executor, critic. The key advantage is task decomposition and parallel execution.

Why Multi-Agent Now?

LLMs are powerful, but one model can’t excel at everything. Multi-agent systems help by:

Distributing complexity: smaller tasks are easier to solve
Parallelizing work: research, synthesis, and comparison run together
Cross-checking outputs: agents review each other to reduce errors
Specializing tools: each agent uses the best tool for the job

Core Building Blocks

1) Roles

Define responsibilities explicitly.

Planner: breaks goals into tasks
Researcher: gathers sources and evidence
Executor: runs code or automation
Critic: reviews and verifies results

2) Shared State

Agents need a place to share context: task boards, document stores, vector DBs.

3) Orchestrator

Controls sequencing, retries, routing, and the overall workflow.

Common Patterns

Pattern	How It Works	Strength	Weakness
Manager–Worker	Leader assigns tasks	Easy to control	Can bottleneck
Debate	Agents argue and vote	Higher quality	Higher cost
Pipeline	Step-by-step handoff	Predictable	Less flexible
Swarm	Loosely coordinated agents	Scales well	Hard to govern

Operational Checklist

Are roles precise? (inputs/outputs defined)
Is failure handling clear? (retries, fallback agent)
Is validation mandatory? (critic, tests, citations)
Are budget and latency limits defined?

Real-World Use Cases

Product Research Automation

Researcher gathers sources, Analyst builds comparisons, Critic validates claims.

Engineering Workflow Automation

Planner decomposes issues, Executor writes/tests code, Critic reviews changes.

Document & Report Production

Writer drafts, Editor refines tone/structure, Fact-checker verifies accuracy.

Pitfalls to Avoid

Role overlap leading to conflict
Context silos causing missing information
No verification amplifying mistakes
Over-parallelization exploding costs

How to Start (MVP)

Start with 2–3 roles (Planner + Executor + Critic)
Fix input/output formats (JSON/Markdown)
Define failure rules (human override after 3 retries)
Measure cost/latency before scaling

Multi-agent systems are not just “smarter models.” They’re better-coordinated teams—and that’s where real leverage appears.

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Multi-Agent Systems: Practical Patterns for Coordinated AI