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This paper identifies the 'progress mirage' failure mode in long-running autonomous LLM agents, where self-evaluation bias causes agents to mistake stagnation for progress. Through controlled experiments, it shows that external, out-of-band verification is necessary for open-ended objectives.
A technical write-up discusses the shift from agent loops to structured graphs in production AI agent work, backed by references to durable execution engines (Temporal, Restate) and research like AFlow which uses Monte Carlo Tree Search to optimize workflow graphs.
The author shares a strategy for reducing costs in multi-model agent loops by using a cheap fast executor for repetitive nodes and a strong planner for high-level reasoning, with experience using Ling-3.0-flash on OpenRouter.
Discusses the overlooked latency cost of decode speed in AI agent loops, affecting overall performance.
This essay by Addy Osmani explores the concept of software factories as scaled agent loops, distinguishing between light factories with human oversight and dark factories without, and emphasizes the importance of understanding and designing the loop, harness, and factory structures.
Introduces Recursive Harness Self-Improvement (RHI), a method that iteratively refines prompt-level harness specifications for AI agents using pairwise feedback, improving performance and reducing inference cost by up to 60% on diverse machine learning research tasks.
The article discusses the usefulness of AI agents while expressing concerns about the potential risks of agent loops.
The article argues that AI coding should use specialized agents in loops with bounded retry and human approval, rather than a single super-agent, emphasizing workflow engineering over agent intelligence.
Explains four types of agent loops—turn-based, goal-based, time-based, and proactive—and how each shifts automation from human to system, helping engineers choose the right structure for different tasks.
Announces TigrimOSR, an open-source agentic AI system built in Rust with customizable agent loops.
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.