loops

An Anthropic engineer explains that instead of writing prompts, they build loops, and demonstrates how the Claude team creates self-prompting loops in a free 42-minute video.

讨论Anthropic工程师如何通过构建循环和图来替代传统提示工程，以及这种方法在系统构建中的应用和潜在成本。

The article explains how using loops for automated checks and graphs for workflow optimization can reduce manual oversight in managing AI agents.

Andrew Ng discusses how self-improving AI agents with loops and graphs are eliminating the need for prompting, offering a free engineering guide to their functionality.

A Chinese developer breaks down the architecture of Grok Bot, explaining why it uses loops and graphs instead of single agents to achieve better performance.

Shubham Saboo discusses how graphs make agent organizations programmable, contrasting static workflow graphs with dynamic agent orgs that rewrite themselves during execution, and cites FarmTable as an example.

The article explains how agent loops become expensive because each step re-sends accumulated context, and advocates for capping costs at the gateway rather than in prompts to prevent unbounded spending.

A developer shares their experience of accidentally discovering 'loop engineering' while submitting 108 pull requests in eight days, highlighting a new approach to software development.

Graph engineering is a new term for coordinating multiple AI agent loops using graphs of nodes (work units) and edges (control flow). The article explains the concept, its historical context (LangGraph, AutoGen, etc.), and the real challenges of designing such graphs.

Ex-Google engineer released a 1-hour course covering self-building agents, RAG memory, and AI loops; claims it beats paid agent courses.

Anthropic published four types of loops for Claude Code to operate autonomously: turn-based, goal-based, time-based, and proactive, allowing different levels of task handoff.

This article introduces the definition and classification of AI agent loops by the Claude Code team, including turn-based, goal-oriented, time-based, and proactive loops, and provides practical advice on controlling token consumption and ensuring code quality.

A practical guide explaining different types of loops (turn-based, goal-based, time-based) for configuring coding agents in Claude Code, including how to define stop conditions and improve self-verification.

A guide to using Fable 5 and Claude Code to create automated workflows (loops and goals) for AI agents, including 25 workflow examples with prompts and tool integration.

This article discusses the critical role of loops in agentic engineering, explaining how throwing more tokens at a problem improves solution quality while addressing the pitfalls of naive loop implementations like compounding errors and lack of meaningful iteration.

Loops introduces goal tracking features to help users measure whether a campaign drove the desired outcome.

The Claude Code team published a blog post introducing the /goal and /loop features. The article analyzes the paradigm shift in AI programming from single-turn conversations to iterative operations, detailing four types of loops (turn-based, goal-based, scheduled, proactive) and their applicable scenarios, while proposing the execution layer concept of Harness Engineering.

A practical guide on setting up iterative loops for AI coding agents with defined stop conditions, cloud execution, and notification channels to offload work without constant babysitting.

PostHog explains why 'loops'—self-prompting agent workflows—are gaining traction, driven by improved model capabilities and real-world results from companies like Stripe and Lovable. The thread details what's needed to engineer a loop and showcases examples like PR babysitting and bug fixing.

Discussion on how loops in AI agents can amplify both good and bad behaviors, emphasizing the need for an engaged human in the loop to guide the agent's learning of user preferences.