Anthropic's 5 workshops on Loop Engineering provide a blueprint for building proactive agents, managed agents, team feedback, evals, and self-improvement loops.

A podcast episode compares OpenAI's GPT-5.6 Sol model to Fable, covering code quality, subagent loops, and Claude Code vs Codex subagent UX.

Designing autonomous loops with a separate verification agent that evaluates executor output against success criteria enables 24/7 task completion without human intervention.

Boris Cherny, creator of Claude Code, writes loops instead of prompts for coding agent workflows, achieving higher automation.

Implementing loops in AI agent workflows dramatically improves success rate, more than the model choice.

Loop engineering—giving an agent a task, objective metric, and stop condition—enables automated SEO, Facebook ads, and product feedback loops that run cheaply and improve over time.

Agentic engineering is about combining agents with deterministic code (linters, tests) in a software factory workflow, not just loops.

Matthew Berman demonstrates a hands-on Codex workflow including model selection, threads, agents.md review, browser use, skills, loops, remote, hooks, and access, with reusable prompts and a loop library.

Agentic engineering is about combining agents, deterministic code, and human review in a software factory workflow, not just loops.

Loop engineering uses AI agents with objective metrics and stop conditions to automate SEO, ads, and feedback loops cheaply.

Agentic engineering means combining agents with deterministic code (linters, tests) in a software factory workflow, not just loops.

Agent loops range from turn-based to proactive, each automating more of the human's role.