AI Engineering/Phase 14/Lesson 31

Agent Workbench Engineering: Why Capable Models Still Fail

A capable model is not enough. Reliable agents need a workbench: instructions, state, scope, feedback, verification, review, and handoff. Strip those away and even a frontier model produces work that is unsafe to ship.

Learn + Build 45 min Python (stdlib)

Prerequisites: Phase 14 · 01 (Agent Loop), Phase 14 · 26 (Failure Modes)

#Learning Objectives

Separate model capability from execution reliability.
Name the seven workbench surfaces that decide whether an agent ships.
Compare a prompt-only run against a workbench-guided run on a small repo task.
Produce a failure-mode report that maps each missed surface to the symptom it caused.

Full Access lesson

Keep going. The rest of this lesson is one step away.

Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.

See plans Sign in

The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.