multi-agent-evaluate

A multi-agent system evaluation examines participant contracts, orchestration, and shared state to validate release readiness. It systematically tests system-level behavior, user journeys, and release gates across happy-path, exception, approval, and recovery scenarios.

A multi-agent system release gate validates coordination, state handling, user experience, and release criteria before deployment. It tests system-level behavior across happy-path, exception, approval, refusal, memory, tool, latency, and recovery scenarios to ensure system stability.

Yes, you can use Python scripts to assess multi-agent orchestration and shared state. The evaluation requires Python scripts and structured memory updates to systematically analyze participant contracts, coordination, and system-level behavior for release readiness.

Yes, multi-agent system evaluation supports exception and refusal scenario testing. It gathers and evaluates data for happy-path, exception, approval, refusal, memory, tool, latency, cost, accessibility, and recovery scenarios to validate release criteria.

The best way to test guardrails and observability in multi-agent systems is through systematic evaluation of participant contracts and shared capabilities. This approach validates system-level behavior, approvals, and release gates to ensure robust coordination and release readiness.

Multi-agent evaluation requires structured memory updates to function effectively and maintain system-level state across evaluations. The maps_memory dependency provides the structured memory needed to track participant contracts, shared state, and coordination metrics during release readiness validation.