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A governance audit pipeline reported success on all 15 runs, yet produced no actual measurements. The exit code alone cannot distinguish a clean result from a failed one, so teams must verify that result files exist before trusting a green light.
A security check that finishes successfully can still leave no results. When the result files are missing, a zero score means "not measured," not "no risk," so teams need a storage rule before they adopt any scoring tool.
Session migration preserves useful work context. But auth, hooks, policy, MCP connections, and runtime config stay with the client. The durable answer is to put approval and audit outside the agent harness entirely.
ADL declaratively defines AI agent roles, permissions, and allowed tools—the OpenAPI of agent governance. Covers the core spec structure and practical EM/CTO governance adoption strategies.
Analyzing research showing LLM agents violate ethics 30-50% of the time under KPI pressure, and discussing governance design for AI agents from an EM perspective.