Introduction to Agent Harnesses

Agent harnesses are a crucial component in building agentic systems, enabling the connection of AI models to external tools, persistence of state across sessions, and management of context. They have become the dominant way to build agents, with various examples including Claude Code, Deep Agents, Pi, OpenCode, and Codex. An agent harness is essentially the complete architectural system surrounding a large-language model (LLM) that manages the lifecycle of context.

How Agent Harnesses Work

An agent harness works by intercepting and augmenting the communication between the user, the AI model, and any external tools or environments. When a tool call is detected, the harness pauses the model’s text generation, executes the requested operation in the outside world, and then feeds the result back into the model’s context. This process enables the AI model to use tools, remember information between steps, and interact with complex environments. The harness also manages tools, memory, and the whole loop of interactions, including prompt engineering.

Importance of Open Agent Harnesses

Using a closed agent harness, especially one behind a proprietary API, can lead to lock-in and loss of control over the agent’s memory. Memory is a critical component of creating good and sticky agentic experiences. Therefore, it is essential to have open agent harnesses, allowing developers to own and manage their agent’s memory. This openness enables greater flexibility, customizability, and autonomy in building agentic systems.

Summary

  • Agent harnesses are a crucial component in building agentic systems, connecting AI models to external tools and managing context.
  • An agent harness works by intercepting and augmenting communication between the user, AI model, and external tools, enabling the model to use tools and remember information between steps.
  • Open agent harnesses are essential to avoid lock-in and maintain control over the agent’s memory, allowing for greater flexibility and customizability in building agentic systems.
  • Examples of agent harnesses include Claude Code, Deep Agents, Pi, OpenCode, and Codex, demonstrating the diversity and range of applications for agent harnesses.
  • By using an open agent harness, developers can focus on building their agent’s core capabilities rather than managing infrastructure, leading to more efficient and effective agentic system development.