Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab
Aug 11, 2026 · 16:57
Aditya Khandelwal, from Amazon AGI Lab, argues that making agents work on a team is a leadership problem, not an IC one, because the fixes that work—restructuring a codebase for progressive disclosure and converging on a shared setup—require team buy-in. He describes symptoms of a bad setup: engineers babysitting runs, burning 500k context on simple tasks, and blaming the model when the harness changed. His team's solution centered on one high-value skill called 'ship it' that carries a change from code done to PR ready, handling descriptions, review comments, and CI failures, often running over an hour. They wired issues and boards into the repo, added agentic reviews, and a nightly code gardener, but agents filing against each other blew the repo to roughly 4,500 open issues in a couple of weeks. In Q&A, he sets a hard limit near 100 lines in a skill file and says first-prompt context burn is the test of whether progressive disclosure works.
