AutoAgent is a tool for autonomous harness engineering that automatically builds and iterates on AI agent configurations.
The tool addresses the problem of manually tuning agent systems by automating the engineering loop. Instead of directly editing harness code, a human provides high-level directives in a Markdown file called program.md, and a meta-agent autonomously modifies the agent's system prompt, tools, configuration, and orchestration. The meta-agent runs benchmarks, evaluates scores, and keeps or discards changes in a hill-climbing optimization loop that can run overnight without human intervention.
The project suits teams building AI agents who want to optimize performance across multiple dimensions simultaneously without manual iteration. It works best when you have a clear benchmark with numeric scores and are willing to let an AI system propose and test changes to your agent harness. The tool isolates agent execution in Docker containers to prevent damage to the host system and uses Harbor-compatible task formats, allowing the same harness to be evaluated against different datasets.
Development activity shows a nascent project with limited public engagement. The repository contains minimal commit history and no evidence of ongoing maintenance or community contributions. The README indicates the team is preparing a commercial product around self-configuring agents and is actively hiring, suggesting this public repository may serve primarily as a reference implementation or early-stage prototype rather than a mature, actively maintained tool. The sparse documentation and lack of example tasks in the repository indicate the project is still in early stages of development.
