Build one, properly
Building one properly means shipping a narrow, production-ready agent as a bounded service: it accepts a defined request, uses explicit tools, runs a controlled loop, records its behaviour, and is evaluated against realistic cases before users rely on it.
The problem is that many agent prototypes look impressive because the task, inputs, and failure cases are vague. That does not survive production. A useful agent needs a job small enough that success can be stated before it runs, such as answering over a known corpus, triaging a class of tickets, completing a report, or checking records. Without that boundary, evaluation becomes taste, debugging becomes guesswork, and every new tool increases the ways the system can fail.
Mechanically, the agent is an application loop around a model, not the model alone. Your code receives the request, builds context, asks the model for the next action, validates that action against tool schemas and policy, executes approved tools, feeds results back, and stops on a clear condition. The important control points live outside the prompt: allowed inputs, timeouts, retries, budgets, fallbacks, output validation, and a trace of each step.
The trade-off is that a properly built agent is less general than a demo assistant. That is intentional. Narrowing the task reduces surprise, but it also means saying no to adjacent requests, limiting tool access, and spending engineering time on tests, traces, and boring failure handling. The honest answer to whether it is reliable is always: it depends on the task boundary, tool quality, evaluation set, and how failures are handled.
Engineers meet this in practice when turning a promising notebook or chat flow into a service. The work is defining the contract, connecting retrieval or tools, implementing the loop, running representative cases, inspecting traces, and deciding whether the observed behaviour is acceptable. A shippable agent should leave enough evidence to explain why it answered, which tools it used, what failed, how long it took, and what it cost.
Common questions
- Is this just prompt engineering?
- No. The prompt is only one part of the system. A production agent also needs code-level boundaries: schemas, tool permissions, validation, retry rules, stop conditions, logging, evaluation data, and fallback paths. If changing a paragraph of instructions is the only control mechanism, the agent is not yet properly engineered.
- Why not give the agent more tools and let it decide?
- More tools can help only when each tool has a clear purpose, safe inputs, and observable results. Otherwise they widen the failure surface. The model may choose the wrong tool, call it with plausible but invalid data, or combine outputs incorrectly. Reliability usually improves by making the tool set smaller and the checks stronger.
- How do I know it is good enough to ship?
- Use representative examples, including normal cases, edge cases, and expected failures. Inspect traces, not just final answers. Check whether the agent chose appropriate actions, used the right context, handled tool errors, stopped correctly, and produced outputs that meet the contract. If you cannot explain failures, it is not ready.
- What is commonly misunderstood about agents?
- The common mistake is treating autonomy as the goal. In production, the goal is controlled usefulness. A good agent is closer to a worker following a checklist than a free-form assistant. The system should make useful decisions inside a narrow lane, while the surrounding code constrains, observes, and verifies what happens.