AutoAgent is an autonomous framework designed for agent engineering, functioning similarly to autoresearch but focused on building and iterating on agent harnesses. The system allows a user to assign a task to an AI agent, which then autonomously modifies system prompts, tools, agent configurations, and orchestration over time. By running benchmarks and checking scores, the meta-agent performs a hill-climbing optimization, keeping improvements and discarding failures. The core workflow involves programming via a Markdown file called program.md, which provides context and directives to the meta-agent, while the meta-agent directly edits the agent.py harness file. This approach minimizes manual engineering by allowing the agent to optimize its own performance through continuous, automated experimentation.
