READING
What the field is saying.
Picked by hand, kept short. These are the clearest posts and papers on agent loops, checks, stopping, and the harness around the model.
Chosen and summarized by hand. Links go straight to the source.
Read this first
↗Already driving Claude Code or Codex one chat at a time? This is the on-ramp from a one-off session to a loop you can trust to run again.
Anthropic · Getting Started with Loops
The clearest first read for turning a one-off Claude Code chat into a repeatable, scheduled loop.
Start here
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My preferred definition of an LLM agent is something that runs tools in a loop to achieve a goal.
Simon Willison · Designing agentic loops
A short, sharp guide to tool choice and stop conditions from one of the clearest writers in the field.
↗Consistently, the most successful implementations use simple, composable patterns rather than complex frameworks.
Anthropic · Building effective agents
The canonical workflow-versus-agent framing, with the simple-loop-first advice every builder should start from.
↗We explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner.
Yao et al. · ReAct
The paper that made the think, act, observe cycle legible as the core shape of modern agents.
Primary Anthropic + Codex sources
AnthropicGetting Started with LoopsThe clearest first read for turning a one-off Claude Code chat into a repeatable, scheduled loop.↗AnthropicBuilding Effective AgentsThe canonical workflow-versus-agent framing, with the simple-loop-first advice every builder should start from.↗AnthropicKeep Claude Working Toward a GoalThe clearest vendor doc for goal loops: a verifiable condition checked after each turn.↗AnthropicMeasuring AI Agent Autonomy in PracticeEvidence that mature agent use means more autonomy plus better interruption, not approval of every action.↗AnthropicEffective harnesses for long-running agentsExplains the harness work needed when a loop runs across many sessions or context windows.↗AnthropicDefine Success Criteria and Build EvaluationsTurns vague agent goals into checks a loop can actually use to decide whether to continue.↗OpenAIUnrolling the Codex Agent LoopA precise look at the inner Codex loop, useful for separating tool-call cycles from pass logs.↗OpenAIUsing Goals in CodexThe Codex-side contract model for goal loops: outcome, checks, constraints, budget, and blockers.↗OpenAIHarness Engineering: Leveraging Codex in an Agent-First WorldThe strongest source for the AI-native engineer role: design the environment, then let agents execute.↗OpenAICustom Instructions with AGENTS.mdHow durable repo guidance becomes agent memory across runs instead of transient chat context.↗
Go deeper
Yao et al.ReAct: Synergizing Reasoning and Acting in Language ModelsThe paper that made the think, act, observe cycle legible as the core shape of modern agents.↗Simon WillisonAn LLM agent runs tools in a loop to achieve a goalThe compact field definition of an agent, and the best first link for the loop idea.↗Sydney Runkle (LangChain)The Art of Loop EngineeringShows how agent, verification, event, and improvement loops stack around each other in real systems.↗Addy OsmaniLoop EngineeringA practitioner-friendly explanation of designing the recurring system around the model, not just prompts.↗Simon WillisonHow coding agents workThe cleanest tour of the loop inside coding agents like Claude Code and Codex.↗AnthropicEffective harnesses for long-running agentsExplains the harness work needed when a loop runs across many sessions or context windows.↗
When you're building for real
AnthropicWriting effective tools for AI agentsTools are the loop's hands, and this is the practical guide to designing them well.↗Hamel HusainYour AI Product Needs EvalsThe best mindset piece on why checks and evals turn repeated model calls into useful products.↗CognitionA review of OpenAI's o1 and how we evaluate coding agentsA production look at how the Devin team decides whether a coding-agent loop is actually done.↗AnthropicEffective context engineering for AI agentsThe advanced companion to harness design: what the agent should see on each pass around the loop.↗