The code around a model that turns it into an agent: the loop, tool execution, memory, limits, stop conditions and logging. Most of an agent's reliability lives here, not in the model.
You have done this if
You wrote the loop that calls the model, runs the tool it picks, feeds back the result and stops after ten steps.
Say it in a review
The model is the easy part; the harness owns budgets, tool permissions, retries and when to stop.
On the AI Application map Agent
Read Why agent behavior is a loop, not a single prompt · Why agents need outcomes, boundaries, and stop conditions