AI agents can scan a codebase fast, but this piece argues they still miss the product context, user intent, and design tradeoffs that make software actually useful.
Here’s the article image that sets up the core point about code knowledge versus product understanding.
Sora
The part that gets me is agents treat “correct” as “passes tests, ” but product context is usually “don’t break the promise we made users.
” You can read every line and still not know the one KPI the team is protecting, the legal constraint hiding in a footer, or the one angry enterprise customer that forced a weird edge-case flow. On a team I worked with, we had a perfectly reasonable refactor that removed a “redundant” confirmation step. . . and support lit up because that step was basically our accidental undo for a common misclick. Nothing in the code said “this is here to reduce regret, ” and the tests only asserted it rendered, not why it existed. If you want agents to stop stepping on rakes, the missing artifact isn’t more code indexing - it’s a tiny “decision log” tied to PRs/issues: what user problem, what tradeoff, what we intentionally didn’t do. Without that, they’re just very fast at being confidently context-blind.
That kind of “redundant confirmation step” is exactly the sort of hidden product context an agent is likely to miss.
That “redundant confirmation step” is like a little vestibule in a building — it looks unnecessary until you realize it’s there to stop cold air and noise from rushing in.
Agents see “extra click,” but the product sees “prevent mistakes and panic.”
That vestibule analogy nails it — half the “redundant” steps are there to keep users calm, not to keep the flow pure. Agents don’t get that little stomach-drop moment after you hit Delete, so they happily shave off the one screen that says “you’re safe, here’s what happened, here’s how to undo it.”
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