Sentry's Greg Pstrucha on why a better prompt won't fix your agent's code

At the AI Engineer World’s Fair, Sentry staff engineer Greg Pstrucha argued that improving AI coding agents depends less on better prompting than on deterministic tooling such as linters, type systems, tests, API schemas, and coding harnesses that prevent recurring basic errors. At Sentry, these safeguards establish a quality baseline for Seer, the company’s debugging agent, while human judgment remains necessary for harder-to-codify codebase policies and semantic evaluations, particularly for agentic behavior and generated fixes. Seer’s evaluations assess not only whether it identifies an issue’s root cause but also whether it produces a correct, high-quality pull request or merge request, with the same engineering checks used for human-written code. Pstrucha cautioned against relying heavily on proxy metrics such as cyclomatic complexity and test coverage because agents can optimize numerical targets without improving real quality. Although Sentry uses automated review tools and engineers increasingly rely on local automated feedback, manual code review remains important for high-stakes production changes; Pstrucha expects reduced review only when automated systems consistently provide reliable quality signals. He characterized current limitations in using agents across mature, multi-service codebases as primarily tooling and infrastructure challenges rather than model-intelligence problems, making basic investments such as strong linters an immediately practical improvement.

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