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Harness Engineering: Anatomy, Architecture, and Evolution of Coding Agents -- A Source-Code Study of Eleven Systems

Source
arxiv.org
Author
Paul Barbaste, Tristan Darrigol, Germain Vu, Tom Wiltberger
Date
From the Daily Brief

A source-code study compares eleven production coding-agent systems, including Claude Code, Codex CLI, Gemini CLI, Aider, OpenHands and OpenCode. The authors map them across seven runtime subsystems and identify 29 recurring implementation patterns. That makes the paper more useful as an architecture reference than another leaderboard because it shows where agent products make similar choices around loops, tools, context, safety, orchestration and extension points. The comparison can help teams separate model capability from harness design when diagnosing why an agent succeeds or fails. It does not prove that every recurring pattern is best, but it gives builders a concrete vocabulary for evaluating their own runtime.

Read the 2026-09-03 Brief →

Context

A coding depends on software around its model: the loop that runs tools, manages working and enforces permissions. A source-code study of eleven coding systems reports that the inspected runtimes used custom execution loops and code-search tools rather than general-purpose agent frameworks or vector-based code retrieval, which searches through numerical representations of meaning.

That finding gives builders concrete architecture patterns to examine. It does not establish that those choices outperform every alternative: the authors inspected implementations rather than running all eleven systems on a shared task set. Their evidence describes how these agents were assembled, not a ranking of their effectiveness.

Terms in this piece · Glossary
  • AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
  • context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
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