🚀 Automated Design of Agentic Systems

The Future of Agentic Systems is Here!

The paper "Automated Design of Agentic Systems" by Shengran Hu, Cong Lu, and Jeff Clune introduces a groundbreaking research area called Automated Design of Agentic Systems (ADAS). The primary goal? To automatically create powerful agentic system designs by inventing novel building blocks and combining them in innovative ways. The core idea revolves around leveraging a meta agent that programs new agents in code, taking full advantage of the Turing completeness of programming languages. This innovative approach is demonstrated through an algorithm named Meta Agent Search.

🔍 Methodology: How Does It Work?

The process is as straightforward as it is revolutionary:

1. Generate: The meta agent generates new agents in code.

2. Test: These agents are tested on specific tasks.

3. Archive: Successful agents are added to an ever-growing archive.

4. Iterate: The archive informs the creation of even better agents in subsequent iterations.

A simple framework is provided to the meta agent, which includes basic functions like querying Foundation Models (FMs) or formatting prompts, allowing the meta agent to focus on defining the "forward" function for new agents.

✨ What Makes ADAS Special?

Unlike previous approaches that focus on manually designing agentic systems or optimizing specific components like prompts, ADAS automates the entire design process. By defining agents in code, this approach unlocks the potential to discover any possible agentic system, including novel prompts, tool use, control flows, and their combinations.

The result? Discovered agents that exhibit superior performance across different domains and models, showcasing their robustness and generality.

📊 Key Findings from Extensive Experiments

The researchers conducted extensive experiments across multiple domains—coding, science, math, reading comprehension, and multi-task problem solving. The results were nothing short of impressive:

• F1 Score Improvements: +13.6/100 on reading comprehension tasks.

• Accuracy Rate Improvements: +14.4% on math tasks.

• Cross-Domain Excellence: Discovered agents maintained superior performance even when transferred across different domains and models.

🎯 The Advantages and Some Cautions

Advantages:

• Automation: Simplifies the design of complex agentic systems, potentially saving enormous amounts of human effort.

• Superior Performance: Outperforms hand-designed agents in robustness and generality.

• Innovation: Enables the discovery of novel design patterns and building blocks.

Limitations:

• Foundation Model Dependency: Relies heavily on the capabilities of current Foundation Models, which may struggle with certain complex tasks.

• Safety Concerns: Executing untrusted model-generated code presents risks that need to be carefully managed.

🌟 Conclusion: A Promising New Era

In conclusion, the paper introduces ADAS as a promising new research area focused on automating the design of agentic systems. The Meta Agent Search algorithm shows significant improvements over hand-designed agents across various domains. While ADAS holds great potential, further research is needed into safe-ADAS and more advanced search algorithms to fully realize its promise.

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