The Anatomy of an Agent Harness

A conceptual framework defining the 'harness' as everything around an LLM that turns it into a working agent. The harness includes system prompts, tools, filesystems, sandboxes, memory, orchestration logic, and middleware. The post derives each harness component by working backwards from desired agent behaviors: durable storage via filesystems, autonomous problem-solving via bash/code execution, safe execution via sandboxes, continual learning via memory and search, context rot mitigation via compaction and tool offloading, and long-horizon execution via planning and self-verification loops. It also covers the co-evolution of model training and harness design, noting that optimizing the harness independently can dramatically improve agent performance on benchmarks.