AI Agents & PR Automation
How AI agents open pull requests, the approval-gate pattern, and safely automating conversion fixes and rollbacks inside a GitHub-based deploy workflow.
- Validating LLM Code Edits When You Can't Run the Code — How an autonomous agent validates code changes to production repos it cannot build, install, or execute: path denylists, byte-anchored edits, provable-only static checks, comparative validation, and an adversarial second model.
- What Is the Approval-Gate Pattern for AI Code Changes? — The approval-gate pattern keeps a human decision between an AI's proposed change and your live code: the agent opens a Pull Request, a person reviews and merges. Here's how and why.
- How to Automate Conversion Fixes with GitHub Pull Requests — Automate conversion fixes as Pull Requests: read analytics to find the leak, generate a scoped code change, open a PR with the reasoning, and keep a human merge. Here's the pipeline.
- Can an AI Agent Open Pull Requests on Your Repo? How It Works — Yes — an AI agent can open Pull Requests via a GitHub App with scoped permissions. It proposes changes you review and merge; it can't merge on its own. Here's the mechanism.
- How to Safely Auto-Roll-Back a Bad Deploy Using Bounce Rate — Auto-roll-back a backfiring change by comparing site-wide bounce rate before and after deploy. If it rises past a set threshold, open a revert PR. Here's the logic and the HogQL.