Build an AI agent by giving a model a bounded task and a controlled way to propose tool actions. The application should validate and authorize every action before executing it.
Define permitted outcomes
Write what success means, which resources the agent may access, and the actions requiring human confirmation. A document assistant that drafts a report needs different permissions from an agent that sends that report externally.
Expose narrow tools
Use explicit argument schemas and server-side checks. Resolve the acting user from authenticated context rather than allowing model-supplied identifiers to determine access. Return only data the task and user are allowed to receive.
Control the execution loop
Enforce step, duration, retry, and spend budgets. Handle cancellation, repeated failures, and partial completion. Use idempotency where retrying an action could create duplicates.
Test adversarial and failed paths
Use synthetic documents and tool responses containing instructions to exceed the task boundary. Verify the application denies unauthorized actions. Exercise timeouts, missing evidence, and a failed confirmation.
Record decisions and outcomes
Trace tool proposals separately from actual executions. Preserve sanitized evidence, permissions, release versions, and the final application state. A plausible final message is not proof that an agent completed its task.
References and further reading
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.