Multi-Agent Systems: When One Agent Isn't Enough

A single AI agent with 50 tools and a 3000-word system prompt will underperform compared to three focused agents with 5 tools each. This mirrors how human teams work — you don't ask one person to do sales, engineering, and accounting. You build a team of specialists.

Multi-agent systems shine in three scenarios: complex workflows with distinct phases (research → analysis → reporting), parallel processing (analyzing 20 documents simultaneously), and tasks requiring different "personalities" (a thorough researcher vs. a concise writer).

The trade-off is complexity. Coordinating multiple agents requires careful design around communication, shared state, error propagation, and resource management. Don't reach for multi-agent when a single well-designed agent would suffice.

Manager-worker pattern

A manager agent receives the task, breaks it into subtasks, delegates to specialist workers, collects results, and synthesizes the final output. This is the most common pattern and works well for tasks like report generation, content creation, and data analysis.

Pipeline pattern

Agents process data sequentially — each agent's output becomes the next agent's input. Useful for multi-stage processing like: extract data → validate → enrich → format → deliver. Each agent is simple and focused, making the system easier to debug and maintain.

Consensus pattern

Multiple agents independently process the same input, and their outputs are compared or merged. Useful for high-stakes decisions where you want multiple perspectives — like code review, content moderation, or risk assessment.

In a multi-agent system, failures cascade if not handled properly. Agent A produces bad output, Agent B acts on it, and by the time you notice, you have a mess.

We implement circuit breakers at each agent boundary. If an agent fails or produces suspicious output (low confidence, unexpected format, inconsistent data), the circuit breaker halts the pipeline and alerts a human rather than propagating the error.

Every agent interaction is logged with full context — what input it received, what it decided, what output it produced. This makes debugging multi-agent failures tractable. Without it, you're debugging a distributed system with no traces.

Timeout handling is critical. An agent that takes 30 seconds instead of 3 probably went into a reasoning loop. Set aggressive timeouts and implement graceful degradation — if the enrichment agent times out, proceed with the base data rather than blocking everything.

Multi-agent systems can get expensive fast. If each agent makes 5 LLM calls and you have 4 agents in a pipeline, that's 20 LLM calls per request. At $0.01 per call, processing 10,000 requests costs $2,000 — before accounting for retries.

We optimize costs by using different model tiers for different agents. The manager agent might use GPT-4o for complex reasoning, while worker agents use Claude Haiku or GPT-4o-mini for simpler tasks. A research agent that just searches and summarizes doesn't need the most expensive model.

Caching is your friend. If two requests ask about the same customer, the second request should reuse the first agent's lookup results rather than making duplicate API calls.

Monitor cost per request in production. Set alerts for cost spikes — they usually indicate a reasoning loop or failed circuit breaker.