Agent Harness: Definition and Role in AI Systems
AI Summary
An agent harness is the software layer that surrounds an AI model, running its execution loop by managing tool calls, supplying context, enforcing permissions, and setting operational limits. While the underlying large language model generates requests, the harness executes them, coordinates interactions, and determines what information the model receives. These architectural decisions directly impact user experience by defining what tasks an AI product can accomplish and how much manual intervention is required from users.
Key Takeaways
- The harness drives the execution loop of request, action, and result between the model and tools.
- It manages context supply, such as passing user preferences and history into the model's working memory.
- It enforces operational constraints like time limits, cost thresholds, step counts, and user approval gates.
Why it matters
Understanding the agent harness helps engineers and UX practitioners look beyond the core AI model to design robust execution loops, secure tool integrations, and effective permission boundaries.
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