advanced Estimated learning time: 20 h

13.4 Capstone 4 — an agent with tools and evals

An agent that completes a real multi-step task, with cost and failure limits.

Before:05. Classical Machine Learning

The final capstone is an agent completing a real multi-step task with tools, cost ceilings and a documented failure taxonomy — proof of engineering autonomy rather than prompting it. It closes the project sequence because it composes the most modules at once. The selection criterion is measurability: a task without a measurable success criterion cannot be evaluated, and it-usually-works is a demo review, not an evaluation.

Work through these

  • Task selection with a measurable success criterion

    A task with a success criterion that can be checked automatically, because an agent you cannot score is an agent you cannot improve. Choosing this well is the hardest part of the capstone.

  • Tool design and error handling

    Tools designed so the model uses them correctly, with sensible behaviour when they fail. Tool errors are the normal case rather than the exception.

  • Tracing, cost per task, step ceilings

    Recording every run, measuring what each task costs, and capping the number of steps. These three are what make an agent safe to leave running.

  • Trajectory evaluation and a documented failure taxonomy

    Evaluating the path as well as the outcome, and writing down the ways it fails. A documented failure taxonomy is more impressive to an interviewer than a success rate.

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Links last checked 29 Aug 2026.

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