Building Multi-Agent AI Systems: Patterns, Pitfalls, and Production

01When do you actually need multiple agents?

You need multiple agents when the work decomposes into specialties that benefit from separate context, separate tools, or separate guardrails — and not before. Most teams over-architect.

02Four coordination patterns

03Pitfall — runaway token cost

Multi-agent systems multiply your token bill. Three agents talking three turns = 9 LLM calls and 9 chunks of context.

04Pitfall — context drift

Each agent only sees its slice. State drifts. Solve it with a shared scratchpad — a single document that every agent reads from and writes to, in a strict schema.

# Every agent reads/writes this structured state.
state = {
  "goal": "Resolve refund #1234",
  "facts": [],          # known facts
  "open_questions": [], # what we still need
  "actions_taken": [],  # log of tool calls
  "next_actor": "researcher"
}

05A working orchestrator example

def orchestrate(task):
    plan = planner.plan(task)
    for step in plan.steps:
        worker = WORKERS[step.role]
        result = worker.execute(step, state=plan.state)
        plan.state.facts.append(result)
        if plan.is_done():
            break
    return synthesizer.write_final_answer(plan.state)

06Test the system, not just the agents

Multi-agent failures emerge from interaction. Your eval suite must cover end-to-end scenarios, not just unit prompts.

07When to retreat to a single agent

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