Agent harness engineering: using evals to improve reliability
Improve an AI agent by evaluating its harness: prompts, tools, context assembly, retries, stop conditions, and orchestration—not only its model.
Agent harness engineering improves the system around a model: instructions, tool interfaces, context selection, memory, retry policies, and control flow. Evals make that work measurable. Instead of changing five pieces and hoping the agent feels better, teams hill-climb one harness decision at a time against a stable task suite.
Model choice matters, but the harness determines what the model can see and do. A capable model with ambiguous tools and unbounded retries can be less reliable than a smaller model inside a clear, constrained loop.
What is an agent harness?
An agent harness is the runtime environment that turns model generations into actions. It usually contains:
- a system prompt and task-specific instructions;
- a registry of tools and their schemas;
- logic for building and trimming context;
- state, memory, and checkpoint handling;
- timeouts, retries, budgets, and stop conditions;
- orchestration for subagents or deterministic steps.
Frameworks expose different abstractions, but these responsibilities remain. For example, LangGraph describes itself as low-level infrastructure for long-running, stateful agents with durable execution and human-in-the-loop controls in its official overview.
Evaluate the harness, not just the final answer
Final-answer accuracy hides the path the agent took. Two runs can return the same answer while one uses the correct tool once and the other loops through six expensive calls. Score both outcome and trajectory.
| Layer | Useful measurements |
|---|---|
| Outcome | Task success, correctness, policy compliance |
| Trajectory | Tool choice, argument validity, unnecessary steps |
| Resources | Tokens, cost, latency, retries |
| Robustness | Recovery from tool errors and missing context |
| Control | Correct stopping, escalation, and human interruption |
Trace each model call and tool span under one run. The trace lets you distinguish “the model chose badly” from “the tool description made two choices look identical.” Our agent observability guide explains the underlying trace structure.
A disciplined hill-climbing loop
- Freeze a baseline. Pin the model, prompt, tools, and runtime settings.
- Build a representative suite. Include normal tasks, edge cases, tool failures, and adversarial instructions.
- Name one hypothesis. For example: shortening tool descriptions will reduce routing mistakes without reducing completion rate.
- Change one harness surface. Keep every unrelated variable fixed.
- Compare paired runs. Evaluate identical tasks with the baseline and candidate, including quality, cost, and trajectory.
- Inspect regressions. Aggregate scores identify movement; traces explain it.
- Promote or revert. Record the winning configuration and add newly found failures to the suite.
Paired evaluation is more sensitive than comparing two unpaired averages. Agent runs are stochastic, so repeat noisy tasks and retain model and environment versions with every result.
High-leverage harness experiments
Tool descriptions are often the first target. Make names distinct, document preconditions, narrow schemas, and validate arguments before side effects. Then measure wrong-tool selection and schema failures.
Context policy is another. Compare full history with summarized or retrieved history. Measure task success alongside input tokens and whether crucial facts survive. A cheaper context policy is not an improvement if it erases the user's constraint.
Finally, test budgets and stopping. Set maximum steps, wall-clock time, and cost; provide an explicit finish or escalation path. Track how often the budget catches a loop versus prematurely stopping valid work. Currai's nested traces make each iteration and tool call visible, while cost-efficiency evaluation keeps cheaper from becoming a synonym for worse.
Prevent benchmark overfitting
A harness can memorize quirks in a small eval set just as a model can. Keep a held-out set, refresh examples from production, and review whether a change solves a general failure class. Do not encode task answers in prompts, tool names, or examples.
Production monitoring completes the loop. Sample new traces, cluster recurring failures, and promote representative examples into offline regression tests. That is how harness engineering compounds: every real failure improves the next evaluation suite instead of becoming a one-off debugging session.