💡 The paper discusses the concept of code as agent harness, where large language models are used as operational substrates for agent reasoning and execution in agentic systems. The authors argue that code is no longer just a target output, but serves as a unified infrastructure layer across multiple domains and applications. They introduce a unified view that centers code as the basis for agent infrastructure, and organize their survey around three connected layers: the harness interface, harness mechanisms, and scaling the harness.

The harness interface layer explores how code connects agents to reasoning, action, and environment modeling. The harness mechanisms layer examines planning, memory, and tool use for long-horizon execution, as well as feedback-driven control and optimization. The scaling layer discusses how to extend the harness from single-agent systems to multi-agent settings, where shared code artifacts support multi-agent coordination, review, and verification.

The authors summarize representative methods and practical applications of code as agent harness, including coding assistants, GUI/OS automation, embodied agents, scientific discovery, personalization and recommendation, DevOps, and enterprise workflows. They also outline open challenges for harness engineering, such as evaluation beyond final task success, verification under incomplete feedback, regression-free harness improvement, consistent shared state across multiple agents, human oversight for safety-critical actions, and extensions to multimodal environments.

The paper provides a unified roadmap toward executable, verifiable, and stateful AI agent systems by centering code as the harness of agentic AI. The authors demonstrate the potential of code as agent harness to enable more efficient, adaptable, and reliable agent systems, and highlight the need for further research in harness engineering to address the open challenges and limitations of this approach. Overall, the paper contributes to the development of agentic systems by providing a new perspective on the role of code in agent infrastructure and highlighting the potential benefits and challenges of this approach.