@dongxi_nlp: Coding Agent Harness, a collection of seven articles.

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Summary

Introduces a tool called Coding Agent Harness and compiles seven related articles, likely covering the development and evaluation framework of coding agents.

Coding Agent Harness, a collection of seven articles.
Original Article

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@dongxi_nlp: https://x.com/dongxi_nlp/status/2065200644802101633

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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.

@dongxi_nlp: https://x.com/dongxi_nlp/status/2066290950352081336

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This article discusses the design concept of how Markdown files (such as AGENTS.md and SKILL.md) in Coding Agents effectively influence agent behavior through the Harness mechanism, emphasizing the importance of loading different contexts at the right time.

@Potatoloogs: https://x.com/Potatoloogs/status/2057391224592667051

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This article deeply analyzes the concept of Agent Harness, which is the engineering infrastructure wrapped around an LLM, including 12 components such as orchestration loops, tool calling, memory systems, context management, etc. The article cites practices from companies like Anthropic, OpenAI, and LangChain, arguing for the critical role of the harness in production-grade AI agents.

@dongxi_nlp: https://x.com/dongxi_nlp/status/2066991890348572950

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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.