Key Points
1. The paper proposes a new research area called Automated Design of Agentic Systems (ADAS), which aims to automatically invent novel building blocks and design powerful agentic systems.
2. The paper argues that ADAS may prove to be the fastest path to developing powerful agents, as it can automate the design of agentic systems rather than relying on manual efforts.
3. The paper shows that there is an unexplored yet promising approach to ADAS where agents can be defined in code, and new agents can be automatically discovered by a "meta" agent programming even better ones in code.
4. The paper presents a simple yet effective algorithm called Meta Agent Search to demonstrate the idea of defining and searching for agents in code.
5. Experiments on the ARC logic puzzle task, reading comprehension, math, science, and multi-task problem solving domains show that the agents discovered by Meta Agent Search substantially outperform state-of-the-art hand-designed baselines.
6. The discovered agents maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality.
7. The paper discusses the potential of ADAS to automate the design of ever-more powerful agentic systems and open up many exciting research directions.
8. The paper emphasizes the importance of developing ADAS safely, given the potential risks of powerful AI systems.
9. The paper concludes that the work illustrates the potential of an exciting new research direction toward fully automating the development of powerful agentic systems.
Summary
This research paper proposes a new research area called Automated Design of Agentic Systems (ADAS), which aims to automatically invent novel building blocks and design powerful agentic systems. The authors argue that this approach may prove to be the fastest path to developing powerful agents, as it can potentially save human effort and lead to more effective solutions than manual design.
The authors present a promising approach to ADAS where agents are defined in code, and new agents can be automatically discovered by a "meta" agent programming even better ones in code. They demonstrate this idea with a simple yet effective algorithm called Meta Agent Search, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries.
Through extensive experiments across multiple domains, including coding, science, and math, the authors show that Meta Agent Search can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Notably, the discovered agents maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality.
The authors discuss the potential of this work to automate the design of agentic systems and highlight the importance of developing it safely. They also outline several future research directions, such as investigating higher-order ADAS, seeding ADAS with more existing building blocks, and exploring more complex domains and multi-objective optimization. Overall, this work presents an exciting new research direction towards the full automation of developing powerful agentic systems, which could have significant implications for the field of artificial intelligence.
Reference: https://arxiv.org/abs/2408.08435