Summary of Automated Design Of Agentic Systems, by Shengran Hu et al.
Automated Design of Agentic Systems
by Shengran Hu, Cong Lu, Jeff Clune
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The researchers are exploring a new area called Automated Design of Agentic Systems (ADAS), which focuses on automatically creating powerful agentic system designs. They demonstrate that by defining agents in code and using a meta agent to program better ones, they can theoretically learn any possible agentic system. The authors present an algorithm called Meta Agent Search that iteratively programs new agents based on previous discoveries. Through extensive experiments across multiple domains, including coding, science, and math, the algorithm is shown to invent agents with novel designs that outperform state-of-the-art hand-designed agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to find a way to make powerful computers automatically. They’re doing this by making new computer programs (called agents) that can create even better programs on their own. This means they could make any kind of program, from ones that help with coding to ones that do science and math problems. The authors show that their method can work well across different areas and makes the best programs. |
