Multi-agent orchestration: patterns and pitfalls to avoid

A single LLM agent isn’t always enough. Sometimes you need multiple collaborating agents. Here are the patterns I’ve tested and what actually works.

Why multiple agents?

Three legitimate reasons to architect multiple agents:

Task complexity exceeds the context window. A single agent that must simultaneously understand a request, search a database, generate a document, and send an email — the context becomes unmanageable.

Specialization improves quality. An agent specialized in classification, another in generation — each with a system prompt optimized for its task — often gives better results than a generalist agent.

Parallelization speeds up processing. Some subtasks can run in parallel if they’re independent.

Patterns that work

Pattern 1: Router + Specialists

A routing agent analyzes the incoming request and directs it to the appropriate specialized agent. Simple, effective, easy to debug.

The router must be simple and precise. Its only job: classify, not respond. The simpler it is, the more reliable.

Pattern 2: Sequential pipeline

Agents pass results in a chain, each enriching or transforming the previous one’s work.

Warning: each step amplifies errors. If Agent 1 extracts poorly, Agent 2 analyzes bad content, Agent 3 formats garbage. Validate each output between steps.

Pattern 3: Supervisor agent

A supervisor agent decomposes a complex task, delegates to sub-agents, aggregates results, and produces the final response.

This is the most powerful but also most fragile pattern. The supervisor must be excellent at decomposing tasks and interpreting sub-agent results.

Classic pitfalls

Chaining agents without validating intermediate outputs. If Agent 1 returns malformed JSON and Agent 2 ingests it without validation, you’ll debug Agent 2 for hours when the problem is in Agent 1.

Too many agents for a simple task. I’ve seen architectures with 7 agents for what a single well-prompted agent would have handled.

No timeout handling. An agent that doesn’t respond must trigger a timeout and fallback.

No traceability. In production, you must know which agent made which decision at each step. Log everything: inputs, outputs, response times, tokens used.

My golden rule

Start with a single agent. Add a second only if you can clearly articulate why one isn’t enough. And so on.

The complexity of a multi-agent architecture is real — in development, maintenance, and debugging. It must be justified by concrete gain, not intuition about what “should work better.”