1. Define the task boundary and authority
AI agent development should begin with a bounded operating responsibility, not a broad promise of autonomy. Define the goal, inputs, allowed decisions, approved tools, evidence of completion, time and cost limits, and the conditions that require human review.
Authority should be narrower than technical access. An agent that can read a customer record does not automatically need permission to modify it. An agent that can draft a transaction should not necessarily submit it. Separate capabilities so consequences remain intentional and auditable.
2. Treat tools as production APIs
Tool descriptions influence which action an agent selects, but safe operation also depends on ordinary API engineering. Each tool needs validated inputs, explicit outputs, authorization, idempotency where actions can repeat, useful errors, timeouts, and observability.
Broad tools create broad failure surfaces. Prefer small capabilities aligned to one responsibility, with the service enforcing policy rather than relying on a prompt to remember it. The agent should request an action; the surrounding system should decide whether that action is permitted.
- Least-privilege data and action scopes
- Schema validation at every boundary
- Idempotent or safely repeatable operations
- Deterministic authorization outside the model
- Traceable tool results and failure reasons
3. Separate workflow state from model context
Conversation history is not a reliable system of record. Durable workflow state should live in application storage with clear status, ownership, version, and recovery behavior. The model receives only the context needed for the next decision.
This separation makes retries, approvals, resumable work, model changes, and incident investigation practical. It also reduces the amount of sensitive or irrelevant information placed into prompts and gives the product a dependable view of what has actually happened.
4. Evaluate the trajectory, not only the answer
A plausible final response can hide a poor route: unnecessary tools, unsafe arguments, repeated actions, unsupported assumptions, or excessive cost. Agent evaluation therefore needs representative tasks and checks across planning, tool choice, state transitions, policy compliance, and completion evidence.
Evaluation should include known success paths, ambiguous inputs, unavailable tools, permission failures, conflicting information, and conditions where the correct behavior is to stop. Production monitoring then connects those test expectations to real trajectories.
- Task completion and evidence quality
- Correct tool selection and arguments
- Policy and approval compliance
- Latency, token, and external-service cost
- Recovery from partial or failed actions
5. Design human review as part of the workflow
Human-in-the-loop control is not a generic confirmation modal. The reviewer needs the proposed action, supporting evidence, uncertainty, consequence, and useful alternatives. The system also needs a record of approval, rejection, modification, and timeout behavior.
The practical production question is not whether an agent appears intelligent. It is whether the complete system can perform useful work within understood limits, reveal what it did, stop safely, and improve without losing accountable human authority.