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Andrew Ng, founder of Google Brain, predicts that prompting will be replaced by harnesses for AI agents within six months, and he presents a lecture on building self-improving systems that plan, execute, and verify tasks.
The article explains why top AI Agent teams are focusing on Harness Engineering, emphasizing that robust runtime scaffolding is crucial for reliable execution in complex scenarios, beyond just model intelligence.
Analysis of 246 open-source repositories and 57 papers on agent harnesses highlights best practices like human-written context, limiting tools, and incremental testing to improve AI agent performance.
Google Research has started researching Recursive Self-Improvement for Agent Harness, focusing on automatically iterating prompts, tools, memory, and control flow without model retraining. This harness-level approach is seen as clearer and more practical than model-level RSI.
The article emphasizes that in AI agents, the harness—comprising tools, context, controls, and workflows—is more critical than the model for achieving reliable, safe, and traceable outcomes.
Harness engineering is a practice to maintain code quality in AI-assisted development by using deterministic tooling and agent-based review to prevent codebase drift and ensure coherence over time.
Two talks and a blog post argue that the feedback loop and harness engineering are more important than model weights for owning AI intelligence in production, highlighting context management and cost considerations.
The article explains harness engineering as a key trend for controlling AI agents by designing deterministic environments, using tools like Google Antigravity SDK, and illustrates how teams can ship software with AI-generated code through sandboxing and repair loops.
This article introduces two books found on GitHub that detail Claude Code's underlying architecture and design philosophy, compare Claude Code with Codex, and help developers understand the design logic of AI coding agents.
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.