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Harness Engineering: Anatomy, Architecture, and Evolution of Coding Agents -- A Source-Code Study of Eleven Systems
AI InsightThis study first decomposes agents into 'model + harness' and systematically dissects eleven production systems, implying that the competitive focus of agents is shifting from model parameters to refined runtime engineering. Harness engineering becoming a distinct discipline signals that future agent differentiation will increasingly depend on infrastructure layers like tool orchestration, context management, and safety controls.Key TakeawayCompetition in coding agents is shifting from model capability to harness (runtime) engineering.Why It MattersAgent effectiveness is heavily influenced by harness architecture, yet systematic empirical research was lacking. This paper provides developers with a comparative map of subsystem implementations, helping lower the barrier to building high-quality agents and driving more stable engineering patterns in the industry.Who's Affected- Coding Agent DevelopersGain a systematic architectural reference for optimizing their harness design.
- LLM Tooling VendorsShould monitor standardization trends in harness engineering and adjust product architecture accordingly.
- Enterprises Adopting AgentsCan use the paper's framework to evaluate and select more mature agent solutions, reducing selection risk.
What's NextWatch for whether this study spawns harness evaluation benchmarks or open-source toolkits, and whether mainstream agents (e.g., Claude Code, Codex CLI) disclose more harness design details, which would validate the framework's practical influence.Importance 72/100