Let the AI Do the Experimenting
Autoresearch is an emerging approach that leverages autonomous AI agents to continuously run experiments, evaluate outcomes, and optimize performance without constant human oversight. The concept, pioneered by Andrej Karpathy and extended by Shopify's pi-autoresearch, enables AI to iterate on tasks like marketing budget optimization under constraints. By automating hypothesis generation and testing, AI can identify high-performing strategies that might otherwise go unexplored.
- ▪Autoresearch was developed by Andrej Karpathy and involves AI agents running self-directed experiments in a continuous loop.
- ▪Shopify open-sourced pi-autoresearch, an extension that uses a terminal-based coding harness to test and refine ideas autonomously.
- ▪The AI evaluates each idea based on whether it improves, worsens, or has no effect on a predefined metric, keeping only beneficial changes.
- ▪The approach was tested on a marketing budget optimization task with a $30M constraint, aiming to maximize projected revenue across campaigns.
- ▪Autoresearch can be applied to any task with a clear objective and measurable outcomes, from code optimization to analytical modeling.
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Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/let-the-ai-do-the-experimenting/ |
| Publication time | Tue, 28 Apr 2026 16:30:00 +0000 |
| Retrieval time | 2026-04-28T16:37:43.353Z |
| Last seen | 2026-04-28T16:37:43.353Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Agentic AI Let the AI Do the Experimenting Using autoresearch to optimize marketing campaigns under budget constraints Mariya Mansurova Apr 28, 2026 14 min read Share Image generated by author with DALLE-3 Have you ever been in a situation where you have plenty of ideas on how to improve your product, but no time to test them all? I bet you have. What if I told you that you no longer have to do it all on your own, you can delegate it to AI. It can run dozens (or even hundreds) of experiments for you, discard ideas that don’t work, and iterate on the ones that actually move the needle. Sounds amazing. And that’s exactly the idea behind autoresearch, where an LLM operates in a loop, continuously experimenting, measuring impact, and iterating from there.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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