Agentic-workflows

Running Claude Code as an autonomous agent inside a GitHub-centric SDLC rests on one discipline: GitHub is the system of record, the agent's conversation is ephemeral, and everything that must survive across pull requests lives in repo files or GitHub itself. This post walks the full issue-to-merge loop — trigger modes, the Explore→Plan→Implement→Commit cycle, cross-session context persistence via CLAUDE.md, the four distinct automated-review surfaces, and the merge gates that keep an agent that literally cannot self-approve from ever merging by fiat. It is built from a fact-checked research pass (25 claims verified, 0 refuted, overwhelmingly first-party Anthropic sources) and is deliberate about separating verified mechanics from the viral stats that did not survive verification.

For two years, getting useful work out of a coding agent meant being the loop yourself — prompt, read, prompt again. As models hold a hard problem for hours, the bottleneck moves: not 'can it write good code' but 'can it keep making progress on its own without losing the thread or declaring victory early.' Loop engineering is the discipline that answers that — you design the system that prompts the agent: discover work, attempt, get a feedback signal, self-correct, verify in a separate context, persist state on disk, decide what's next. This post lays out the architecture, the five building blocks, a worked worker/verifier loop in Python, the loops worth building first, the best practices, and an honest look at the risks (the 'confident token furnace'), with every flow rendered as a diagram.