Five multi-agent architecture patterns that hold up

Start with the task, not the agent count

A multi-agent design is useful only when a task benefits from separable responsibilities, independent work, or a controlled transfer of ownership. A second agent does not automatically improve reasoning. It adds another prompt, context boundary, model call, failure mode, and place where data can be mishandled.

Write down the user outcome, inputs, allowed actions, and acceptance test before choosing orchestration. First build the smallest workflow that could pass that test. Split a role only when you can name the measurable benefit, such as parallel latency reduction or isolation of a privileged tool.

• Define the unit of success for one complete user request
• Set a maximum number of model calls, tool calls, and handoffs
• Make authorization checks in the tool or service, never in model instructions alone

Five orchestration patterns

These patterns describe control flow, not a requirement to create five separate agents. A single application can combine them, but each extra transition should have an explicit reason.

• Sequential pipeline: fixed stages pass validated outputs forward; best when dependencies and order are known
• Concurrent specialists: independent workers examine separate subtasks; useful when parallel work saves time and outputs can be reconciled
• Handoff: one agent transfers a task to a specialist that becomes the active owner; define what context and responsibility transfer
• Supervisor and workers: a coordinator decomposes work and reviews results; bound delegation depth and verify the final synthesis
• Group collaboration: participants exchange multiple turns; use only when interaction changes the result enough to justify unpredictable latency and cost

Choose the control boundary deliberately

For fixed business processes, a deterministic workflow often gives clearer auditability than an autonomous supervisor. Use model choice for genuinely ambiguous decisions, while application code enforces required order, permissions, schemas, retry limits, and approval gates.

Keep shared state minimal. Pass a typed task envelope containing the request identifier, user authorization context, objective, allowed tools, output schema, and deadline. Do not let an agent silently widen its own permissions when a handoff occurs.

• Validate every worker result before another worker consumes it
• Treat retrieved content and tool output as untrusted input
• Record agent identity, tool decision, result, and policy outcome without logging secrets

Evaluate the whole system

A successful sub-agent response is not the same as a successful user task. Build test cases that cover normal requests, ambiguous goals, tool errors, conflicting evidence, prompt injection, and a specialist returning invalid output. Compare the same cases against a single-agent or deterministic baseline.

Track task completion, answer quality, policy violations, tool-call precision, end-to-end latency, cost per successful task, and recovery behavior. Report both averages and important tail cases; a mean score can hide a small set of consequential failures.

• Test routing and delegation separately from final answer quality
• Include a no-action or escalation outcome when evidence is insufficient
• Set release thresholds before looking at the candidate system's results

A practical rollout

Begin with one agent and a small, explicit tool set. Add one specialist for a bottleneck or a boundary that you have measured. Re-run the same regression set, estimate incremental operating cost, and retain the design only if the improvement is material and explainable.

Document why the pattern was selected, alternatives rejected, failure ownership, data exchanged, and rollback plan. This makes architecture review more useful than a diagram that simply labels every component an agent.

Frequently asked questions