13.05 · Walkthrough
Build one, properly
Ship a bounded, evaluated, observable agent that does one real job.
A production agent is a narrow service with explicit tools, controlled execution, evaluation data, traces, limits and fallbacks. The useful version does one defined job, records what happened, and can be judged against expected behaviour before users rely on it.
What this lesson answers
- how to build a production ready ai agent
- what makes an ai agent safe to ship
- how to evaluate and observe an agent
Notes
A shippable agent is not a chatbot with extra freedom. It is a small service that accepts a specific kind of request, uses a limited set of tools, follows a controlled loop, and produces an auditable result. The job should be narrow enough that you can say what success means before running it: answer questions over this documentation, triage these tickets, fill this report, or reconcile these records.
The mental model is a production worker with a checklist, not a genius intern. The model proposes actions, but your code owns the boundaries: tool schemas, allowed inputs, retries, timeouts,…
Common questions
- What is the difference between a chatbot and a shippable agent?
- A chatbot mainly responds to open-ended messages. A shippable agent has a defined contract, a narrow task, approved tools, stop conditions, validation and traceable outputs. It behaves more like a small service than a general assistant, so failures can be tested, debugged and contained.
- Why should an agent have fewer tools and less autonomy?
- Every extra tool and choice increases the paths the system can take, which makes testing harder and failures less predictable. A reliable agent constrains what it can do, checks intermediate results, handles errors explicitly and keeps the impact of a bad model decision small.
- What should be logged for a production agent?
- Log the original request, retrieved context, selected tools, tool inputs and outputs, model responses, validation failures, retries, errors, latency, cost signals and final result. These traces are what let you explain behaviour, find weak cases and decide whether changes made the agent better.
Short definition: what is Build one, properly?