Cadre

Capstone 16 — GitHub Issue-to-PR Autonomous Agent

AWS Remote SWE Agents, Cursor Background Agents, OpenAI Codex cloud, and Google Jules all ship the same 2026 product shape: label an issue, get a PR. Run an agent in a cloud sandbox, verify tests pass, and post a review-ready PR with rationale. The hard parts are reproducing the repo's build environment automatically, preventing credential leakage, enforcing per-repo budgets, and making sure the agent cannot force-push. This capstone builds the self-hosted version and compares it on cost and pass rate to the hosted alternatives.

Capstone 30 hr Python (agent), TypeScript (GitHub App), YAML (Actions)

Prerequisites: Phase 11 (LLM engineering), Phase 13 (tools), Phase 14 (agents), Phase 15 (autonomous), Phase 17 (infrastructure)

#Problem

The async cloud coding agent is a separate product category from interactive coding agents (capstone 01). The UX is a GitHub label. You label an issue @agent fix this, a worker spins up in a cloud sandbox, clones the repo, runs tests, edits files, verifies, and opens a PR with the agent's rationale in the body. No interactive loop, no terminal. AWS Remote SWE Agents, Cursor Background Agents, OpenAI Codex cloud, Google Jules, and Factory Droids all converge on this.

The engineering challenges are concrete: environment reproduction (the agent has to build the repo from scratch without a cached dev image), flaky tests (must be re-run or isolated), credential scoping (a GitHub App with minimal fine-grained permissions), budget enforcement per repo per day, and no-force-push policy. The capstone measures pass rate, cost, and safety vs the hosted alternatives.

#Problem

Keep going. The rest of this lesson is one step away.