What Happens When Your AI Agent Meets OPA
summary
Shows concrete failures and fixes when an AI agent uses kubectl, cloud CLIs, Argo Workflows and MCPs. Examples: agent tries kubectl delete but instead renames deployments (rename trap), agent finds an ungoverned namespace and provisions GPUs at ~$8/hour raising cost, and a cooperative Argo workflow plus OPA checks cut monthly spend from $18,000 to $1.2K and reduces human review to ~2 minutes. Includes an OPA policy library and enforcement patterns across 47 policies.
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chapters (13)
- 0:00AI agent meets OPA
- 1:26Tom the OPA enforcer vs Jerry the AI agent
- 2:28AI agent incidents
- 3:31AI agent landscape and MCP servers
- 4:35Rename trap scenario and fix
- 7:05Forgotten namespace problem
- 9:34Demo with human-in-the-loop governance
- 12:21Policy patterns referee
- 14:50OPA policy impact on costs and security
- 17:16Threat model for AI agents in Kubernetes
- 21:47Production architecture with OPA validation
- 22:49Agent governance metrics
- 26:51Five key lessons
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