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Loop Engineering

Loop Engineering is the practice of designing recurring systems for AI agents and coding agents. Instead of prompting an agent turn by turn, you build a loop that discovers work, delegates it to one or more agents, verifies the result against tests or other deterministic gates, persists state outside the model, decides what happens next, and runs again on a cadence, an event, or until a verifiable goal is reached. It sits above prompt, context, and harness engineering: those improve a single run, while loop engineering governs repeated agent work over time, including budgets, retries, escalation to humans, and stopping conditions.

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Claude Code dynamic workflow for autonomous software engineering. Zero human intervention. Spec-driven, doc-as-ground-truth pipeline that persists across sessions. Agents understand the full project — won't fix local and break global.

  • Updated Jun 10, 2026
  • JavaScript

Prompt → context → loop → spiral: is the system running your loops verifiably improving? A falsifiability contract (pitch · grounding · null-ratio), zero-dep instruments, a real ledger — 7/7 skills Δ>0, measured for under $1. A loop returns to where it started; a spiral proves it didn't.

  • Updated Jul 16, 2026
  • JavaScript
pipeshape-claude-plugins

Claude Code plugin that builds deterministic multi-agent dev pipelines — define the graph in YAML, scaffold agents into your repo, run with spec gates, feedback loops, and resume.

  • Updated Sep 16, 2026
  • JavaScript