eta-Harness is an advanced optimization system designed to improve how language-model agents process and compress long interaction histories into useful states. Unlike traditional methods that rely on manual engineering or simple feedback, this system uses a coding agent to search for and rewrite the "harness" code that manages an agent's memory and retrieval. By providing the proposer with direct filesystem access to raw execution traces and historical performance data, it avoids the information loss associated with summarized feedback. This approach allows the system to discover superior strategies for history summarization and adaptive retrieval across various complex tasks. Experimental results demonstrate that Meta-Harness achieves top-tier performance on benchmarks like TerminalBench-2 and improves accuracy in mathematical reasoning and text classification. Ultimately, the research suggests that the way agents construct their own internal state can be optimized as an embedded learning problem.