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Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab

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AI Engineer
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AI Engineer
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Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
  • agent skill — A reusable instruction file that teaches an agent how to do one job well — the procedure, the tools, and what counts as done.
  • context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters

It is one of the few accounts of rolling coding agents across a whole team rather than one enthusiast, with the org-level changes named. The -size and -burn heuristics are directly reusable.

Key quotes

“Leave agent adoption to individuals and the engineer shipping two PRs a day ends up reviewing the ten that the early adopter ships.”

“adoption is therefore a leadership problem and not an IC one, because the changes that actually work, restructuring a codebase for progressive disclosure and converging on a shared setup, are not changes one engineer can make alone.”

Aditya Khandelwal

“Someone says the model got dumb today, when the model did not change and the harness did.”

“Their fix centered on one high value skill, called ship it, that carries a change from code done to PR ready, handling the description, the review comments, and CI failures, and often runs for over an hour.”

“It was not clean: agents filing against each other took the repo to roughly 4,500 open issues in a couple of weeks.”

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