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

Description and use

AI coding agents can fix bugs, but without real‑time production data they are ineffective. Integrating production session data lets the agent quickly identify and fix issues.

Problem

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

Outcome

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

Workflow

• 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.

Evidence

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

Source

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

Source

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

Source