Agent Harness Engineering: A Survey (picrew.github.io)

A recent survey titled "Agent Harness Engineering: A Survey" introduces agent harness engineering as a crucial, independent system layer that enhances the reliability of large language model agents in practical deployments. The authors propose a comprehensive seven-layer taxonomy called ETCLOVG—Execution, Tooling, Context, Lifecycle, Observability, Verification, and Governance—highlighting that the effectiveness of these agents hinges more on their execution infrastructure than solely on the models themselves. The survey systematically maps various open-source projects onto this taxonomy, revealing coverage gaps and patterns in the adoption of robust operational designs. This work is significant for the AI/ML community as it emphasizes a shift in focus from individual model performance to the orchestration of diverse systems that govern how agents operate. Notably, the survey identifies a trend towards developing agent platforms over traditional frameworks, which integrate capabilities for observability, evaluation, and governance across multiple deployments. By establishing the harness as a multi-faceted entity with interconnected layers, it encourages researchers and practitioners to consider holistic solutions that address systemic constraints rather than viewing the components in isolation, thus laying the groundwork for future innovations in agent design and operation.