Harness Engineering: The Execution Layer AI Agents Actually Need

This post introduces the concept of 'Harness Engineering' as a critical discipline for building reliable autonomous AI agents. It details how Harness Engineering relies on hybrid search (semantic and keyword) for agents to access information from live, evolving knowledge bases. The post outlines specific challenges within Harness Engineering, such as context anxiety and self-evaluation bias, and proposes solutions involving structured documentation, layered architectures, and adversarial agent designs (e.g., Planner, Generator, Evaluator). It highlights the need for retrieval systems that can perform both semantic and exact-match queries concurrently to support agent decision-making.