AI Agent Readiness Brief: RAG Evaluation and Tool Risk: RAG evaluation and tool-risk review are often handled by different people. That split is dangerous. A grounded answer can still trigger the wrong tool, and a well-scoped tool can still act on weak evidence.
Evaluate evidence before action
A tool-using agent should not treat retrieval success as action approval. Before a write, send, refund, delete, or account-change tool runs, the system should know which source supports the action and whether that source is current, scoped, and allowed for the user.
Separate answer quality from action readiness
A RAG answer can be mostly correct but still not ready to drive automation. For action readiness, check source freshness, tenant boundary, policy conflict, confidence, and whether missing fields should pause the workflow.
Make tool calls auditable
Every important tool call should record the user request, retrieved sources, selected tool, arguments, approval status, and final result. Without that trace, the team cannot explain why the agent acted.
Add refusal and pause cases
Tests should include unsupported claims, conflicting policies, stale documents, and injected instructions inside retrieved text. The agent should pause or escalate rather than turning weak retrieval into a confident action.
Use a release gate
Before launch, require a sample of successful and failed runs. Review whether the agent cited the right sources, respected permissions, and stopped when context was missing.
I would use this for an AI agent launch review: AI Agent Readiness Brief: RAG Evaluation and Tool Risk. It turns risk into concrete checks instead of abstract advice.Related resources
- RAG Evaluation Checklist
- AI Agent Tool Approval Policy Template
- Free AI Agent Production Readiness Checklist Template
Next step
If these issues already affect real users or customer data, run the AI Agent Readiness Self-Assessment first, then turn blockers into a launch checklist.