Building a Multi-Agent Team: Lessons Learned in Orchestration

While a single AI agent can accomplish a lot, true enterprise automation requires a team. Orchestrating a multi-agent system introduces new challenges: handoff protocols, shared context, and error recovery.

The Architecture of a Team

In our recent builds, we moved away from monolithic "do-it-all" agents and split responsibilities into specialized roles:

1. The Triage Router: Scans incoming requests and assigns them to the right specialist.
2. The Execution Specialist: Performs the actual work (e.g., writing code, generating reports).
3. The QA Reviewer: Validates the output against predefined success criteria before delivering it to the user.

Key Lessons in Orchestration

1. Standardize Handoff Protocols

Agents need a structured way to pass tasks. We implemented a strict JSON schema for inter-agent communication. When the Executor finishes, it doesn't just say "Done"—it passes a payload containing the task_id, status, and artifact_links.

2. Global State is Non-Negotiable

If Agent A learns something that Agent B needs, passing it through the chat context is inefficient. We introduced a centralized state manager where agents can check out and update the status of a job asynchronously.

3. Graceful Degradation

When a specialist fails, the system shouldn't crash. Instead, the orchestrator catches the failure, logs the error, and either retries with a fallback model or escalates to a human operator.

Building a multi-agent team isn't just about AI; it's about system design. Treat your agents like microservices, and the orchestration becomes much clearer.