AI Agent Governance Framework: 4 Layers for QA Teams

AI agent governance for QA teams: a 4-layer framework (Identity, Permission, Verification, Observation) to verify what your AI agents actually do before they ship. 👉 Try Agent Assurance: https://www.testmuai.com/agent-assurance/?utm_source=youtube&utm_medium=organic&utm_campaign=ai_agent_governance AI agent governance isn't only about access policies. A response that says "done" doesn't prove the job was done right. In this video: ✅ What AI agent governance means: policies, identity, access control, verification and monitoring ✅ Why testing autonomous agents is different from testing scripts ✅ How multi-agent hand-offs and duplicate refunds slip past guardrails ✅ Policy-based vs. evidence-based governance ✅ The Agent Trust Stack: Identity, Permission, Verification, Observation ✅ Using Agent Assurance (Rook CLI) to get Pass / Fail / Unable to Verify results before release 🔗 Start free with TestMu AI: https://www.testmuai.com/ 📘 Agent Assurance docs: https://www.testmuai.com/support/docs/agent-assurance-overview/ ⏱️ Chapters 00:00 Why AI agents need governance 01:05 What is AI agent governance? 01:45 Testing scripts vs. autonomous agents 02:00 Travel booking agent: "booked" isn't proof 02:57 KPMG: 62% are building AI agents 03:17 Multi-agent hand-off failures 04:10 Why guardrails missed a duplicate refund 04:51 Policy-based vs. evidence-based governance 05:13 Agent Trust Stack: Identity & Permission 06:00 Layer 3: Behavioral verification 07:09 Layer 4: Observation after deployment 07:53 What your agent eval actually measures 08:16 Test oracle: verify outcomes, not claims 08:58 Why agents can't grade their own work 09:18 TestMu AI Agent Assurance walkthrough 09:50 Pass, Fail, Unable to Verify + release gate 10:24 Completing the Agent Trust Stack #AIAgents #AgentGovernance #AIAgentTesting #TestMuAI