My AI Coding Agent Is Brilliant Until the Bug Is in Production

Описание и применение

AI‑кодинг‑агенты могут исправлять ошибки, но без данных о реальном поведении пользователей они неэффективны. Интеграция наблюдений из продакшн‑сессий позволяет агенту быстро находить и исправлять баги.

Задача

AI coding agents can modify code based on repository knowledge, but they lack real‑time production context, leading to ineffective bug fixes.

Результат

By feeding production evidence into AI agents, debugging becomes faster and more accurate, reducing manual detective work.

Рабочий процесс

• 1. Production system detects a bug (e.g., checkout failure).
• 2. Session replay and logs capture user actions and errors.
• 3. HeronSignal aggregates evidence and presents it to the AI agent.
• 4. AI agent analyzes production data and repository code to identify root cause.
• 5. AI generates a fix and drafts a pull request.
• 6. Developer reviews the PR and decides whether to merge.

Доказательства

The agent knows 40,000 lines of my codebase. It just doesn’t know what happened 30 seconds ago.

Источник

Instead of treating production context as something only a developer reads, they’re making it available to the agent too.

Источник

HeronAgent can go from the production interaction to the repository, investigate the relevant code, prepare a fix and open the PR.

Источник