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| aliases: agent-harnesses, ai-agent-harness, llm-harness | ||
| display_name: Agent Harness | ||
| short_description: Runtime infrastructure that connects LLMs to real-world tools, memory, and execution environments. | ||
| topic: agent-harness | ||
| related: | ||
| - ai-agent | ||
| - orchestration | ||
| - tool-use | ||
| - mcp | ||
| - llm | ||
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| --- | ||
| <!-- markdownlint-disable MD041 --> | ||
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| An **agent harness** is the runtime layer between a large language model and the outside world. It manages the agentic loop — prompt the model, parse tool calls, execute tools, feed results back — while enforcing permissions, compressing context, and handling error recovery. The term gained traction after Anthropic's Claude Code source was studied, revealing the internal architecture that turns a text-generating LLM into a capable software-engineering agent. | ||
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| Agent harness projects include full runtime environments (LobeHub, oh-my-agent, HexAgent, Utah), agent harness frameworks for Python and TypeScript (water, Chorus), code-quality agents (desloppify), portable skill systems (cc-harness-skills, oh-my-agent), research surveys (LLM-Agent-Harness-Survey), and autonomous systems with persistent memory and orchestration (Adam, Sibyl). The pattern is characterized by a tool registry, a permission system, context management, and often multi-agent coordination. | ||
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