Building production-ready AI agents is often a grueling cycle of manual prompt tweaking and reactive debugging. You change a single line in a system prompt, run an evaluation, see it fail on a different edge case, and repeat the process indefinitely.
This constant back-and-forth makes scaling agentic workflows incredibly difficult. If you want your production agents to stop making mistakes, you need a way to move from manual tinkering to automated optimization.
The Problem: The Manual Optimization Trap
When an agent fails in the wild, the instinct is to manually adjust the prompt or middleware. However, this trial-and-error approach is slow, unscalable, and often leads to regressions that are difficult to track over time. Before you can automate these fixes, it is essential to have a robust evaluation framework in place, similar to mastering LLM evaluation with Promptfoo.
Without a structured way to measure success, you are essentially flying blind. You need a system that can run evals overnight and return with an optimized harness while you sleep.
The Solution: Autoharness as Your Control Plane
This is where autoharness comes in. Rather than being a standalone tool that replaces your workflow, it acts as a control plane for your existing harness repository. It manages the heavy lifting of proposing changes and running benchmarks to find the best possible configuration.
By pointing it at a target root and a benchmark command, the autoharness repository allows you to automate the search for better prompts, configs, middleware, and even source code changes. It keeps or discards candidates based strictly on your benchmark results.
| Component | Description |
|---|---|
| Target Root | The harness repo or deployment tree to edit. |
| Benchmark Config | The command or adapter config that scores candidates. |
| Workspace | The long-lived optimization effort. |
| Track | One comparable lane inside a workspace. |
| Campaign | A resumable search run over candidate proposals. |
How the Optimization Loop Works
Autoharness follows a structured lifecycle to ensure that every change is validated before it becomes part of your production stack. The tool provides several key commands to manage this process:
- guide: Inspects your repo and helps you set up a starter configuration via an interactive TTY session.
- doctor: Validates your config, generator, and benchmark setup to ensure everything is ready for execution.
- run-benchmark: Executes a single benchmark directly to verify current performance.
- generate-proposal: Previews a potential candidate change without actually running it.
- optimize: Runs the full, resumable search loop to find the best candidates.
Getting Started with Autoharness
Setting up autoharness is designed to be seamless, especially if you use AI coding assistants like Codex or Claude Code. You can simply point your assistant at the repo and let it handle the onboarding packet.
For a standard installation via pipx, run the following command in your terminal:
pipx install "git+https://github.com/kayba-ai/autoharness.git"If you prefer using standard pip, use this command instead:
python3 -m pip install --user "git+https://github.com/kayba-ai/autoharness.git"Once installed, run autoharness guide to begin your first optimization campaign. This will walk you through the setup and create a .autoharness/ directory where all your results, proposals, and champions are persisted.
Conclusion
Stop wasting time on manual prompt engineering. By implementing an automated control plane like autoharness, you can ensure your agents are constantly evolving toward higher accuracy and reliability. Let the machine handle the iterations so you can focus on building the next generation of AI applications.
Ready to optimize? Clone autoharness today and start your first optimization campaign tonight.