AI safety does not stop at the model

AI agent safety requires not only auditing actions after they occur but also enforcing clear limits on what agents may do before execution. While frontier AI labs must decide which capabilities to release, deploying companies are responsible for determining the authority granted to agents, including applicable policies, approval requirements, and credential access. These controls must remain reliable through production failures, timeouts, and retries so that an interrupted or rejected action cannot bypass established limits. Using a travel-booking example, the author argues that retries should preserve an approved $900 fare rather than revert to an unapproved $2,000 option. The author presents Durable Execution as a way to preserve approvals, rejections, and outcomes across failures, and describes Temporal Agent Harness as an execution-layer control point that works alongside existing agent development tools while leaving companies responsible for their own policies, identities, permissions, and security systems.

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