Multi-agent systems: when one AI isn't enough.

The first instinct when building an AI system is to make one agent do everything. One agent with one big prompt and access to all the tools. This works well for focused, single-domain tasks. It starts to break down for complex, multi-step tasks that involve different types of work, large amounts of context, or work that can be done in parallel.

Multi-agent architectures are the answer to this breakdown — and understanding when and how to use them is one of the most important design skills in AI engineering.

Three reasons to use multiple agents

Context window constraints. Language models have finite context windows. A task that requires processing 500 pages of documents, analyzing them, and producing a synthesis can't fit in a single prompt. A multi-agent approach handles this by having specialized agents process subsets of the documents in parallel and having an orchestrator synthesize the results. The orchestrator never sees the full 500 pages — it sees the synthesized outputs from agents that each read a portion.

Specialization. Different parts of a complex task may benefit from different prompts, different models, or different tools. A research pipeline might need: a retrieval agent that finds relevant documents (optimized for recall), an analysis agent that extracts structured information (optimized for accuracy), a synthesis agent that combines the analyses (optimized for coherence), and a critic agent that reviews the synthesis for errors (optimized for skeptical review). Each of these benefits from its own focused system prompt; combining them in one agent produces mediocre results on all four.

Parallel execution. If subtasks are independent, running them in parallel with multiple agents is dramatically faster than running them sequentially with one. A competitive analysis of 10 companies can run 10 specialized research agents simultaneously and synthesize in a fraction of the time it would take to analyze them serially.

The orchestrator-worker pattern

The most common multi-agent architecture is orchestrator-worker. The orchestrator agent receives the top-level task, breaks it into subtasks, assigns those subtasks to worker agents, monitors completion, and synthesizes the results into a final output.

The orchestrator's responsibilities:

• Task decomposition: breaking the goal into concrete, assignable subtasks
• Agent assignment: routing each subtask to the appropriate specialized agent
• State management: tracking which subtasks are complete and what each returned
• Error handling: deciding what to do when a worker fails or returns low-confidence output
• Synthesis: combining worker outputs into a coherent final result

The worker agents' responsibilities are deliberately narrow: receive a well-defined task, execute it using their tools, return a structured output. Workers don't need to understand the broader context — they just need to do their specific part well.

The critic pattern

One of the most powerful patterns in multi-agent systems is using one agent to evaluate the work of another. A generator agent produces an output; a critic agent reviews it against specific criteria and identifies errors, omissions, or quality issues.

This pattern consistently improves output quality in ways that making the generator more sophisticated doesn't. The generator optimizes for producing an answer; the critic optimizes for finding what's wrong with that answer. These are different cognitive modes, and having separate agents for each produces better results than asking one agent to do both.

Practical applications of the critic pattern:

• A contract review agent generates a risk summary; a critic agent checks for clauses the first agent might have missed
• A proposal writing agent drafts the first version; a critic agent reviews it against the requirements and flags gaps
• A data extraction agent pulls structured data; a validator agent checks it against schema and business logic rules

Common design mistakes in multi-agent systems

Using multiple agents when one would do. Multi-agent systems are more complex, more expensive, and harder to debug. Don't use them unless there's a specific reason a single agent won't work. The three reasons above (context constraints, specialization benefits, and parallelism needs) are the legitimate ones. "It sounds more advanced" is not.

Poor inter-agent communication design. The interface between agents — what one agent passes to another — is one of the most important design decisions in a multi-agent system. Vague or unstructured handoffs produce downstream quality problems that are hard to trace. Define the output schema for each agent explicitly and validate it before passing to the next agent.

No error handling for agent failures. In a sequential multi-agent pipeline, a failure at step 3 leaves you with no output from steps 4–6. Design explicit failure handling: what happens if an agent returns low confidence? What happens if it times out? What's the fallback? Systems without defined failure handling produce unpredictable behavior when they encounter real-world edge cases.

Missing observability. When a multi-agent system produces a wrong final answer, tracing the error back to its source is much harder than in a single-agent system. Build logging at every agent handoff. Log inputs, outputs, confidence scores, and any errors. Without this, debugging production issues takes days instead of hours.

When to escalate to a human

The most reliable multi-agent systems have defined human escalation paths. When the orchestrator can't decompose a task because it's ambiguous, escalate. When a worker returns an output below the confidence threshold, escalate. When the critic identifies a significant issue the generator can't resolve, escalate.

The human escalation path should be as carefully designed as the agent execution path. It should include: who gets notified, what context they receive, what action they're being asked to take, and how their decision gets fed back into the system. Agents that fail silently are worse than agents that fail visibly.

Multi-agent systems are the right architecture for a class of complex tasks that single agents can't handle well. They're also significantly more complex to build, test, and maintain. Design upward to multi-agent when you have a specific reason to; don't start there by default.

If you're working on a task that might need a multi-agent approach, book a call. We help teams evaluate whether the complexity is warranted and design the architecture correctly from the start.