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Automated Design of Agentic Systems (ADAS) represents an innovative research direction that leverages meta-learning algorithms to create increasingly powerful and versatile agents. These automatically discovered agents not only outperform manual solutions but also demonstrate remarkable robustness and transferability across different domains.

Key Points:

Meta Agent Search: An algorithm that enables a meta-agent to iteratively create, test and improve new agents, leading to superior performance compared to traditional methods.

Turing Completeness: The use of complete Turing programming languages allows the exploration of any possible combination of agent modules, from tools to control flows.

Cross-Cutting Applications: Automatically discovered agents are effective in a wide range of domains, including science, mathematics, and coding, demonstrating remarkable adaptability.

Sustainable Development: ADAS promises to accelerate the creation of powerful agents, reducing the cost and time required compared to traditional methods

Artificial intelligence research is making great progress toward the development of powerful and versatile agent systems capable of tackling complex tasks through basic models such as GPT and Claude. However, the history of machine learning suggests that manual solutions are often replaced by learned solutions that are more efficient. This has led to the emergence of a new research area, called “Automated Design of Agentic Systems” (ADAS), which aims to automatically design agentic systems, including the discovery of new modules or the innovative combination of existing ones.

One promising approach within ADAS involves defining agents in code, allowing a meta-agent to iteratively program new and improved agents. This method takes advantage of the Turing Completeness of programming languages, theoretically allowing the learning of any possible agent system, from prompts to control flows and tool usage.

The core of the proposed algorithm, called “Meta Agent Search,” is to have a meta-agent create new agents, evaluate them, and use the knowledge gained to further improve the process. Experiments have shown that automatically discovered agents far outperform hand-designed agents in various domains, including coding, science, and mathematics. These agents, in addition to excelling in their specific tasks, are shown to be robust and transferable across different domains and models, suggesting high generality.

 The work on ADAS not only represents an exciting research direction but also a potentially faster and less expensive way to develop increasingly powerful and useful agents for humanity.