Specialised agents working together, each owning a domain of the overall task.
Multi-agent collaboration is the pattern that enables AI systems to tackle tasks that are too complex, broad, or multi-disciplinary for a single agent. Each agent has a defined specialty, a bounded scope, and a clear interface for exchanging information with other agents.
A multi-agent system defines a set of specialised agents, each with its own system prompt, tool access, and scope. When a complex task arrives, it is decomposed into components that align with agent specialties. Agents communicate via structured messages — either directly with each other or via a shared message bus. An orchestrating layer (which may itself be an agent) tracks the overall task state, manages handoffs between agents, and decides when the task is complete. The key design principle is that each agent should be independently testable: you should be able to evaluate Agent A's performance without running Agent B or C.
An HR department deploys a multi-agent system for employee onboarding. When a new hire is confirmed, four specialist agents activate: an IT provisioning agent (creates accounts, requests hardware), a payroll agent (sets up payroll run, benefits enrollment), a facilities agent (allocates desk, orders access card), and an onboarding content agent (schedules orientation, assigns learning paths). An orchestrator tracks completion across all four. Tasks that require input from multiple agents — like matching the new hire's start date to the next payroll run — are handled by the orchestrator, not passed between specialist agents.
Single-agent systems attempting to cover multiple domains produce worse results than specialist agents and are harder to audit because their scope is undefined. Multi-agent collaboration enables domain expertise, independent evaluation, and clear accountability. When something goes wrong, you know which agent was responsible.
How this pattern fails in practice — and what to watch for.
Two agents receive conflicting information about the same task — for example, the HR agent and the payroll agent each believe they are responsible for communicating the start date to the new hire. The new hire receives two different messages. Neither agent knows the conflict occurred.
Agent A is waiting for Agent B's output before proceeding. Agent B is waiting for a confirmation from Agent A. Neither can proceed. Without a timeout and escalation mechanism, the task stalls indefinitely without alerting anyone.
A task falls in the space between two agents' defined scopes. Neither agent claims it. The task is silently dropped. The gap is only discovered when a downstream process fails because an expected output was never produced.
Seven things to verify before deploying this pattern in production.
Multi-agent collaboration is the highest-complexity pattern in the AIDA exam, covering D5 and D6 simultaneously. The exam presents scenarios with coordination failures and asks candidates to identify the architectural cause. CAIG examines accountability: in a multi-agent system, who is responsible for an output that required five agents to produce? CAIAUD auditors are assessed on their ability to trace an outcome back through agent interactions to its root cause.
The AIDA certification covers all 21 agentic design patterns with a focus on deployment safety, governance, and the PSF. Free to attempt.