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Auto Agent: Self-Improving AI Harnesses Inspired by Karpathy’s Auto-Research Loop The video explains self-improving agents and highlights Kevin Guo’s Auto Agent project as an extension of Andrej Karpathy’s auto-research idea. Auto-research lets an AI agent iteratively edit training code (e.g., train.py) under a small LLM training setup, run short trainings, evaluate results, and keep or discard changes based on improvement, guided by human-written instructions in program.md. Auto Agent applies the same loop to a different target: optimizing the agent harness itself (prompts, tools, orchestration) rather than ML training code. It uses a meta-agent and a task agent, connects to benchmarks via an adapter, and runs many parallel sandboxes to evaluate iterations using results and reasoning traces. Examples include SpreadsheetBench and TerminalBench, illustrating harness improvements and the broader implications for domain-specific workflows and cheaper, specialized agent setups. Links; https://x.com/karpathy/status/2030371219518931079 https://github.com/karpathy/autoresearch https://x.com/kevingu/status/2039843234760073341 https://github.com/kevinrgu/autoagent/blob/main/program.md 00:00 Self Improving Agents 00:33 Auto Research Recap 01:25 Why Simplicity Worked 02:22 Auto Agent Architecture 03:20 Benchmarks And Results 03:52 Why Harness Optimization Matters 04:36 Future Of Meta Agents 05:01 Wrap Up
Technical content at the intersection of AI and development. Building with AI agents, Claude Code, and modern dev tools - then showing you exactly how it works.
