An agentic harness is the wrapper around an LLM that turns it into an agent by running it in a loop, giving it tools, and managing state.
Working Definition
Agentic harness = controller loop + tool runtime + context/state management + guardrails + (optional) evaluation.
In practice, the harness is the part that:
- Calls the model repeatedly (plan -> act -> observe -> update -> repeat)
- Executes tools (shell, files, web, APIs, repo ops) on the model’s behalf
- Manages context (what to include, summarize, persist, retrieve)
- Enforces constraints (step limits, timeouts, budgets, sandboxing, policy checks)
- Optionally adds evaluation (tests, graders, benchmarks, self-checks)
Two Common Meanings
- Runtime harness - used to build/operate real agents (coding agents, research agents, ops agents).
- Evaluation harness - used to run task suites and measure performance across models/variants.
Why It Matters
Most “agent capability” comes from the harness:
- Better tool schemas and error handling -> fewer dead ends
- Better context strategy -> less thrashing and hallucination
- Better guardrails -> safer automation and fewer expensive loops
- Better eval harness -> faster iteration and trustworthy improvements
Quick Checklist
A good harness usually has:
- Clear step protocol (actions vs observations)
- Tool contracts (schemas, retries, timeouts)
- Memory strategy (short-term summary + retrieval + persistent notes)
- Budgeting (tokens, time, steps, cost caps)
- Evals (unit tests, lint, golden tasks, regression suite)