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. 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. You drop a frontier model into a real repo and ask it to add input validation. It opens four files, writes plausible code, declares success, and stops. You run the tests. Two fail. A third file is touched that had nothing to do with validation. There is no record of what the agent assumed, what it tried first, or what is left to do. The model was not wrong about Python. It was wrong about the work. It had no idea what counted as done, where it was allowed to write, what tests were authoritative, or how the next session was supposed to pick up. This is not a model bug. It is a workbench bug. The surface around the agent is missing the parts that turn a one-shot generation into reliable, resumable engineering. A workbench is the operating environment that wraps…
Agent Workbench Engineering: Why Capable Models Still Fail: A capable model is not enough. Reliable agents need a workbench: instructions, state, scope,…
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