Tag
A fix for token latency in sequential agent loops is presented, where parallel tool calling improves performance in LLM agent systems.
Paul Solt shares a detailed workflow for using Codex agents in a loop to autonomously build features overnight, including a manager thread, heartbeat, and automated PR review. The technique shifts from single prompts to designed agent loops, enabling continuous development with minimal human intervention.
A free website called Loop Library provides a collection of repeatable AI agent workflows and prompts for engineering tasks like documentation, testing, and performance optimization, helping AI engineers avoid writing agent loops from scratch.
Discusses approaches to make AI agent loops less reliant on prompts and more deterministic, aiming for greater reliability and control in agentic systems.
An ex-Google engineer presents a 20-minute explanation of AI agent loops, harness, and evals, offering a framework for self-improvement through tracing, judging with LLM, diagnosing, fixing, and shipping.
A tweet predicts that AI product managers hired in 2026 will focus on self-healing agent loops.
A detailed roadmap for transitioning from a single AI agent to a coordinated swarm of agents, covering when to split, how to run parallel subagents without conflicts, and how to maintain sanity at scale using Claude Code primitives.
The author argues that a single giant agent is ineffective for running a company, and describes their approach with Matrix, an operating system for autonomous work that organizes agents into a workspace brain, department leads, and scoped workers with proof loops.
A critique of AI agent loops that continue without reasoning, suggesting that agents should pause periodically to analyze failures and propose theories before retrying.
The article explains the shift from manually prompting coding agents to designing automated loops that prompt them, detailing what these loops are, their historical evolution, and the components needed to build them in production.
A curated collection of pre-built agent loops (closed-loop workflows) for coding agents like Claude Code and Cursor, including triggers, feedback gates, and exit conditions for self-paced task execution.
Loop engineering is the practice of designing systems where AI agents autonomously decide what to work on, execute, and iterate, going beyond manual prompting by building outer loops that compound across different domains. The article explains the two-layer agent harness and how sharing artifacts between loops creates compounding learning.
The article distills 28 research papers into a 10-layer stack for building self-improving harnesses around AI models, emphasizing bounded, gated changes over general agent loops.
A curated list of practical /loop, /goal, and /schedule commands for Claude Code and Codex, shared by power users on X/Twitter, collected in an awesome-list GitHub repository for copy-paste use.
A technical teardown of how multi-agent frameworks like CrewAI and AutoGen actually route information under the hood, revealing that they are essentially automated prompt-chaining loops. The article explains why agents get stuck in infinite loops due to context window inflation and missing deterministic stop conditions, offering practical advice for builders to treat agents as functional programming functions instead of human collaborators.
The author discusses the need for a fourth governance loop in self-improving AI agent systems to prevent objective drift, proposing periodic human review, withheld benchmarks, and rotating evaluators as practical controls.
This article discusses how AI coding agent loops can inadvertently learn and propagate deprecated code patterns from existing codebases, leading to technical debt despite appearing successful.
A researcher asks AI agent builders about common failures in production, including tool failures, agent loops, context loss, and debugging practices.
The article argues that the primary failure point for AI agents in production is not the model itself, but the lack of infrastructure such as stop buttons, billing oversight, and traceability for tool calls.
IntiDev AgentLoops is an open-source tool that provides feedback loops for agentic workflows, hosted on GitHub.