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The author argues that RL environments serve as the essential data for building AI agents, enabling systematic training, prompt optimization, and evaluation rather than manual iteration.
Omar recommends reading Chamath Palihapitiya's AI investing guide, emphasizing the importance of harness engineering and evals for AI builders.
The author reflects on how long-running AI agents encounter failures unrelated to the initial prompt, arguing that environment design (tools, docs, validation, architecture rules) matters more. They discuss concepts like harness engineering, keeping AGENTS.md small, using linters, and evaluator agents, while noting the cost trade-offs.
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.
An analysis of five major trends from the AI Engineer World's Fair 2026, highlighting the maturation of AI engineering practices such as coding agents, harness engineering, and system reliability, with a shift from agent-focused development to building robust systems around models.
A recap of five major trends from the 2026 AI Engineer World's Fair, showing how AI engineering has matured from prompt engineering to building reliable systems, with a shift from agents to harnesses, loop engineering, enterprise adoption, coding agents replacing IDEs, and skill-based agent platforms.
The author shares a personal experience of using harness engineering and free NVIDIA NIM endpoints to build AI agents capable of automating complex multi-step workflows, such as creating AI video series and hotel booking systems, suggesting that web crawling services can be replaced by free frontier models.
Recommends and translates Lilian Weng's blog article on Harness Engineering for Self-Improvement, detailing the concept of recursive self-improvement (RSI), patterns of harness (workflow automation, filesystem persistent memory, sub-agents), and a coding agent case study.
Introduces a harness engineering approach for building auditable enterprise LLM agents by moving deterministic behavior into code, schemas, and validation artifacts, demonstrated on Korean corporate data with fault-injection and model-substitution tests.
Former OpenAI engineer Ryan Lopopolo joins Google Cloud as Chief Agent Engineer, bringing OpenAI's Agent engineering methodology to the cloud platform.
This blog post by Lilian Weng explores the concept of recursive self-improvement in AI, focusing on how harness engineering—the system surrounding base models—enables automation and improvement of AI agents through workflow design and evaluation.
Lilian Weng's blog post argues that recursive self-improvement (RSI) in AI will be realized through refining the design and optimization of the 'harness' (the system surrounding the model), and highlights research examples from Sakana AI.
Lilian Weng's blog post explores the concept of harness engineering as a key component for recursive self-improvement in AI systems, discussing design patterns, workflow automation, and the analogy to operating systems.
This article shares the team's practice of drawing on OpenAI's Harness engineering philosophy to enable an AI Agent to run autonomously for 17 hours with 16 iterations of prompt optimization, and successfully launch the project, including key mechanisms such as anti-cheating and preventing early stopping.
A recommendation to examine winning harnesses from the ARC AGI challenge to understand effective first-principles design and avoid overfitting to benchmarks.
A curated collection of resources on harness engineering for AI coding agents, including a course, a Python tool (tau-ai), and blog posts from Anthropic and LangChain.
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.
Describes DR-DCI, an optimization that combines RAG with bash commands on a virtual filesystem to enable agents to perform precise corpus retrieval, and discusses scaling to distributed systems for inference providers.
A discussion about the buzz around Harness Engineering and AI agent Harness, questioning whether the industry is moving away from LLMs deciding agent responses.
The author gave a talk at Google on AI Agent Harness Engineering, demonstrating a Financial Agent using Gemma 4 local LLMs that ran under 15 GB RAM and performed on par with frontier models.