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Structured Evidence Handoffs Make Multi-Agent AI Diagnosis More Reliable

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In multi-agent AI operations workflows, the reliability of an orchestrator's output depends directly on the quality of evidence passed back by its sub-agents. A proposed approach structures these handoffs as verifiable evidence packets rather than plain assertions, improving auditability. The method also applies elimination-based reasoning across service topologies to narrow down root causes more systematically. Past incidents can be replayed as regression tests, allowing teams to validate and refine diagnostic accuracy over time. Together, these practices aim to make agentic AI systems more transparent and trustworthy in production environments.

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