Bonus dive

#Professional Tools: A Guided Deep Dive

Every other repo in this series builds a primitive from scratch (the agent loop, the retriever, the eval harness, the guardrails) because the primitive is what transfers. This repo is the second half of that bargain: the tools professionals actually reach for on the job, learned against the versions you already built by hand.

CAREERS.md maps each dive to the industry tools that productionize it, so you can recognize them in a job posting. Recognizing a tool and having used it are different interview answers. This dive closes that gap, and does it the only way this series knows how: measured, against a baseline you understand, with the tradeoffs shown honestly.

New here? Read WALKTHROUGH.md first. It is the guided tour of the whole journey: the setup that gates each chapter, and for every tool the fork in the road, the gotcha that will trip you, and the best and worst case for reaching for it. This README is the reference; the walkthrough is the map with the potholes marked.

#The method: port, measure, verdict

Every chapter follows the same shape:

1. The primitive. A short recap of what you hand-rolled in the earlier dive, and a link to it. That's the baseline: you know exactly what it does, because you wrote every line.
2. The tool. The professional replacement, introduced by what it claims to automate, not by its marketing page.
3. The port. Rebuild the same artifact with the tool. Minimal, runnable, side by side with the hand-rolled version.
4. The measurement. Run the same eval against both. Not vibes: the retrieval metrics, red-team attack success rates, and judge scores you built in the earlier dives are the measuring stick here.
5. The verdict. What the tool bought you (less code, maintained integrations, tracing for free), what it cost you (debugging opacity, version churn, an abstraction between you and the request), and when you'd still hand-roll. Written from the run you just did, not from the docs.

The point is never "the framework is better" or "frameworks are bloat." The point is that after each chapter you can say, in an interview and in a design review: "I built the primitive, I've used the tool, and here is precisely what the tool is doing for me."

#The chapters

One representative tool per category; the survey breadth lives in CAREERS.md; depth lives here. Order mirrors the original series path. The slate was verified against mid-2026 usage (surveys, adoption data), not habit, e.g. promptfoo lost its slot to DeepEval after OpenAI acquired it in March 2026. That verification date is part of the repo's honesty: check the landscape again before trusting the slate in 2027.

Chapters land incrementally; the status column is the source of truth. A chapter isn't "done" until its verdict is backed by a real measured run; see the series' authoring principles: the output is the argument.

#Prerequisites

This is a volume 2. Each chapter assumes you've done the corresponding from-scratch dive (or at least can read its final artifact comfortably); the whole teaching device is the comparison against something you understand. Start with the series path if you haven't.

#A warning this repo wears on purpose

This repo will rot faster than the others, by design. Frameworks rename modules, break APIs between minor versions, and fall out of fashion, and that churn is itself one of the lessons, and it's the reason the rest of the series teaches primitives first. Dependencies are pinned per chapter so the examples keep running; when a pin drifts far behind the tool's current release, treat the concepts in the verdict as current and the code as a snapshot. The primitive underneath doesn't move.

Part of the AI Engineering deep-dive series sequenced, self-contained, from-scratch lessons. This dive is the bridge from "I built it by hand" to "I've run the tool professionals use, and I know what it's doing."