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agentic-loops

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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Loop until it's better — drop-in agentic loops (autoresearch, scientific writing, data analysis, code/SQL/prompt optimization, red-teaming) as open-standard Agent Skills. Verification-gated; native on Claude Code, portable across Codex, Cursor & other Skills hosts.

  • Updated Jun 30, 2026
  • Python

Run a company of AI agents on a server you own. Spin up named agents (claude, codex, grok…), put them on an org chart with a shared backlog, let them hand off work and ping your phone only when a human must decide. MIT.

  • Updated Jul 12, 2026
  • Shell