harness

This article points out that 80% of AI Agent production crashes are not due to model intelligence, but are caused by context overflow, tool misconfiguration, and sub-agent runaway. The author emphasizes that the watershed in 2026 lies in Harness (office systems, security) and Loop (automatic cycling mechanism), not the model itself.

A discussion question about whether to evaluate a machine learning harness as a whole or evaluate its individual components separately.

A discussion about the focus of AI evaluations, questioning whether practitioners are optimizing prompts, context, or the entire harness, and noting a shift toward holistic optimization.

A brief prediction that in 2025 engineers will integrate LLM APIs into their test harnesses, and in 2026 they will design harnesses to work within their agents.

Discusses the emerging pattern of using external harness loops to extend AI coding agent sessions beyond normal boundaries, and critiques current code quality issues.

This article explains the concept of loop engineering in AI agents, emphasizing that the core loop is trivial but the critical work lies in the harness around the model, including knowing when to stop and preventing context rot.

This is the sixth article in the series, explaining in detail the concept of subagent, its working principles, and its role in coding agents, including tool call and runtime mechanisms, as well as the applicable scenarios of different subagent types (fresh child, forked child, partial fork).

This article deeply explains the importance of the evaluation framework (Harness) in AI, analyzes the strategic significance of DeepSeek building its own Harness team, and compares the differences between the open-source lm-evaluation-harness and an in-house system.

MetaHarness converts any GitHub repository into a custom AI agent harness with CLI, MCP service, memory, and signing, allowing deployment on multiple agent platforms.

Matt Pocock argues that the AI community is overly focused on models themselves, and that the real key is the harness (tooling/framework) surrounding them.

This is the 6th article in the "Context Is A Projection Harness" series. It delves into the core issues of context management in coding agents, proposing a Harness method that projects the full history into the narrow window needed by the model. Key techniques include Large-Result Preview, Idle-Gap Microcompact, Old-Span Collapse, and Auto-Compact Near The Limit.

The DeepSeek Harness team is in urgent need of talent; the hiring policy has been changed to separate Harness and non-Harness tracks.

A step-by-step guide to building a minimal AI coding agent that runs entirely locally using llama.cpp, GGUF models, and a custom harness, demonstrating how to set up tools and call a model to execute real tasks like creating a landing page.

Yoyo is an AI agent that self-evolves every 8 hours on GitHub Actions. Its key to success lies in a harness design of a stateless agent plus persistent state (git repository). The article deeply analyzes simple solutions to issues such as memory, context, feedback, verification, etc., emphasizing that persistent state is more critical than the model itself.

The author argues that an AI agent is best understood as a folder of markdown files containing business knowledge and instructions, separate from the model and harness, enabling portability between rapidly improving harnesses.

Researchers from UCL reverse-engineered Claude Code, finding that only 1.6% of the codebase is AI decision logic while 98.4% is operational infrastructure, revealing a design philosophy that prioritizes a rich deterministic harness over model-driven routing.

An experiment with a local governance harness for AI coding agents shows that when the agent's own governance record is surfaced in its context, the agent begins to self-correct by following policies and asking for intent declarations, without hard enforcement.

The article proposes that in a Coding Agent, tool invocations should be treated as contracts rather than simple functions, emphasizing the Harness's adjudication role in verification, permissions, lifecycle management, and others, and discusses in detail the composition and lifecycle of tool contracts.

Introduces HarnessBridge, a learnable bidirectional controller that parameterizes the agent-environment interface for LLM agents, achieving performance comparable to specialized harnesses with reduced computational overhead on Terminal-Bench and SWE-bench.

This video explains the concept of an AI agent harness: the LLM core, memory, tools, and the loop that enables iterative decision-making toward a goal.