What Your Coding Agent Can't See in the Code - Tom Moor, Linear
Source
youtube.com
Author
AI Engineer
Date
Why it matters
Shows how to feed agents product history (reverts, incidents, past decisions) so reviews catch problems a standard code reviewer misses, using triage, review and lesson-extraction loops.
Key takeaways · AI-distilled
Tom Moor of Linear argues writing code is no longer the bottleneck; the harder problem is keeping a product coherent as agents ship more and more of it.
Linear's triage AI agentAn 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.Full definition → enriches each issue with customer and incident context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition →, and its review agent checks changes against past decisions, reverts and regressions.
A daily automation at Linear turns review feedback into lessons stored in the codebase, and Moor shows real cases where that history caught problems a standard code reviewer would have missed.
Terms in this piece · Glossary
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
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.
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