This paper introduces Meta-Harness, an innovative outer-loop system designed to automate the optimization of model harnesses for large language model (LLM) applications. While traditional harnesses are largely designed by hand, Meta-Harness employs an agentic proposer that searches over harness code by accessing source code, scores, and execution traces. The researchers demonstrate significant performance gains across multiple domains: improving text classification efficiency, enhancing accuracy in retrieval-augmented math reasoning for IMO-level problems, and surpassing hand-engineered baselines in agentic coding tasks. The results suggest that providing automated systems with richer access to prior experience can successfully enable the automated engineering of complex LLM harnesses.
This article explores the concept of an "agent harness," the essential software infrastructure that wraps around a Large Language Model (LLM) to enable autonomous, goal-directed behavior. While foundation models provide the core reasoning capabilities, the harness manages the orchestration loop, tool integration, memory, context management, state persistence, and error handling. The author breaks down the eleven critical components of a production-grade harness, drawing insights from industry leaders such as Anthropic, OpenAI, and LangChain. By comparing the harness to an operating system and the LLM to a CPU, the piece provides a technical framework for understanding how to move from simple demos to robust, production-ready AI agents.
This article by Sebastian Raschka explores the fundamental architecture of coding agents and agent harnesses. Rather than focusing solely on the raw capabilities of Large Language Models, the author delves into the surrounding software layers—the "harness"—that enable effective software engineering tasks. The piece identifies six critical components: providing live repository context, optimizing prompt shapes for cache reuse, implementing structured tool access, managing context bloat through clipping and summarization, maintaining structured session memory, and utilizing bounded subagents for task delegation. By examining these building blocks, the article illustrates how a well-designed system can significantly enhance the practical utility of both standard and reasoning models in complex coding environments.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
