An AI agent is often described as a model that can plan and use tools. That description is useful, but incomplete for production engineering. A production agent is a system that repeatedly observes state, chooses from a limited set of actions, checks what happened, and either continues or asks a person to take over.
The goal is not to make an agent autonomous at any cost. The goal is to make a workflow more useful while keeping its behavior understandable and bounded.
Begin with one job
Choose a task with a clear start, a clear finish, and a way to tell whether it was completed correctly. “Help our operations team resolve refund requests” is a better starting point than “run customer support.” The first can be decomposed into steps and reviewed; the second hides many policies and edge cases.
Write down the inputs the agent can see, the actions it can take, and the outcomes it must never produce. This small contract becomes the foundation for tool design and evaluation.
Make tools narrow and explicit
Tools are the agent’s permissions expressed as software interfaces. Prefer actions such as look_up_order or draft_refund_recommendation over a generic tool that can execute arbitrary queries or make unrestricted changes.
A useful tool boundary has:
- Validated inputs and structured outputs.
- A clear authorization check for the current user and resource.
- An idempotency strategy for actions that may be retried.
- A record of what was requested and what the system actually did.
For consequential actions—issuing a refund, changing access, sending an external message—separate the recommendation from the execution. Require a person or a deterministic policy to approve the action.
Put guardrails around the loop
The orchestration layer should enforce a maximum number of steps, a deadline, token or cost limits, and an allowlist of tools. Validate every tool result before it returns to the model. Treat retrieved documents and tool outputs as untrusted input: they can contain incorrect or adversarial instructions.
When the agent is uncertain, a tool fails, or the task falls outside its contract, return a clear handoff instead of trying indefinitely. A graceful stop is a successful system behavior.
Observe runs, not just model calls
Capture a trace for each run: input references, model and prompt version, tool calls, validation outcomes, latency, cost, and final status. Avoid logging secrets or unnecessary personal information. These traces make it possible to debug a specific failure and identify patterns across many runs.
Evaluate with real task examples before rollout. Measure task completion, policy violations, unnecessary tool use, escalation quality, latency, and cost. Include difficult and ambiguous cases—not only happy paths—and rerun the evaluation when prompts, models, tools, or data change.
Ship in stages
Start with offline evaluations, then a read-only or recommendation-only mode, then a small supervised pilot. Expand permissions only when results support the change. Keep a way to pause the workflow and fall back to the existing process.
A capable model matters, but dependable agentic AI comes from the boundaries around it: a narrow job, constrained tools, visible execution, and a reliable human handoff. Build those foundations first, then increase autonomy deliberately.