Harness Engineering Guide
- Source
- github.com
- Author
- nexu
- Date
Context
The Harness Engineering Guide explains what turns a language model into an . It defines the as the surrounding program that handles what the model cannot do alone: executing tools, managing memory, assembling and enforcing safety boundaries. The guide describes the core as a think, act, observe loop. The model asks for an action, the harness carries it out, and the result returns to the model, with turn budgets limiting how long the cycle runs.
The guide then moves from that loop toward production concerns. It opens with a short Python harness meant to be copied and run, and treats permissions, and error handling as separate topics. For a learner, that ordering marks a boundary: a working loop shows the mechanism, while giving an agent broader access depends on safeguards covered elsewhere in the guide.
- agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
- context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
- sandbox — An isolated environment where AI-generated code or agent actions run without being able to touch anything real.
“A harness is the runtime wrapper that turns a bare language model into an agent — an autonomous system that can perceive its environment, make decisions, and take actions over multiple steps.”
“The harness handles everything the model can't do on its own: executing tools, managing memory, assembling context, and enforcing safety boundaries.”
“This guide covers harness engineering from first principles to production patterns, with real code in every article.”
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