An AI agent is useful only when it can execute bounded tasks reliably, recover from failures and produce verifiable outcomes.
Research context: educational synthesis. Verify primary studies, dates, methods and independent replications before attributing a specific breakthrough to this reporting week.
Agents may plan and call tools, but a plausible response is not evidence that an external task was completed.
Specify measurable task completion, permitted actions, deadlines and failure conditions before evaluation.
Grant minimum necessary permissions and separate reading, proposing and executing consequential actions.
Record tool calls, inputs, outputs, timestamps and approvals while protecting sensitive information.
Test timeouts, incorrect tool results, repeated actions and safe rollback or escalation paths.
Require explicit human authorization for high-impact, irreversible or financially consequential actions.
Use held-out tasks, end-to-end success rates, error severity, cost and reproducibility across runs.
Build an observe–plan–act–verify workflow with audit logs, stop conditions and measurable business outcomes.