An agent combines uncertainty with action
An AI agent differs from a static answer tool because it can select steps, use tools and change external state. That flexibility is valuable, but model errors can become sent messages, incorrect records or unintended transactions if permissions are broad.
Choose a bounded workflow with clear inputs, allowed actions, success criteria and a safe fallback. Early use cases should be reversible and observable, such as drafting, classifying or preparing an action for approval.
Design boundaries before capability
Grant only the tools and data needed for the task, with scoped credentials and explicit confirmation before consequential actions. Separate retrieval from instruction, validate structured inputs and treat external content as untrusted.
Define when the agent must stop, ask for clarification or hand off. Confidence is not a guarantee of correctness, so critical decisions need deterministic checks or accountable human review.
- Define scope, success and prohibited actions.
- Use least-privilege tools and credentials.
- Require confirmation for consequential changes.
- Maintain evaluations, logs and rollback paths.
Evaluate the workflow continuously
Evaluate realistic normal, edge and adversarial cases before release. Measure task completion, factuality, policy compliance, unnecessary escalation, latency, cost and the quality of recovery when tools fail.
Log prompts, tool calls, outputs and approvals with appropriate privacy controls. Review failures, update tests and version changes so the organisation knows which behaviour is in production.