Learning outcomes
- Explain the operating model behind Agent Action Verification.
- Evaluate trade-offs and failure modes for Agent Action Verification.
- Apply Agent Action Verification to a production decision.
Mental model
Action verification compares authorized intent, submitted parameters, tool acknowledgement, and authoritative postcondition state.
Learning outcomes
- Explain the mechanism and ownership boundaries behind Agent Action Verification.
- Compare the main design alternatives and their operational trade-offs.
- Diagnose common failures and select evidence for a production decision.
Theory
A strong verifier is independent of the model that proposed the action. It checks object identity, scope, version, side effects, and postconditions, then records evidence for evaluation and recovery.
Trade-offs
Independent reads add latency and load but catch false acknowledgements and partial effects. Trusting tool return text is faster but creates a weak success boundary.
Failure modes and misconceptions
Verifying only HTTP status; reading stale replicas; checking the wrong tenant; letting the proposer grade itself; and omitting negative postconditions.
Decision scenario
A deployment tool returns success, but traffic has not shifted. Define the authoritative checks required before the agent closes the task.
What is the difference between a successful tool response and a verified business postcondition?
Primary sources
anthropic-trustworthy-agentsopenai-evals
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Agent Action Verification while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Agent Action Verification builds on Tool Calling.
Agent Action Verification informs governed production decisions and review evidence.
Primary sources
- Evaluation Best Practices — OpenAI, verified 2026-07-16
- Trustworthy Agents in Practice — Anthropic, verified 2026-07-21