Automated Design of Agentic Systems

Original Paper: https://arxiv.org/abs/2408.08435

By: Shengran Hu, Cong Lu, Jeff Clune

Abstract:

Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, 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, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.

Summary Notes

Figure: Overview of the proposed algorithm Meta Agent Search and examples of discovered agents. In our algorithm, we instruct the “meta” agent to iteratively program new agents, test their performance on tasks, add them to an archive of discovered agents, and use this archive to inform the meta agent in subsequent iterations. We show three example agents across our runs, with all names generated by the meta agent. The detailed code of example agents can be found in Appendix F.

Introduction

Imagine a world where intelligent systems can design themselves, evolving iteratively to solve increasingly complex tasks. This is not merely the stuff of science fiction but the reality being brought to life by researchers Shengran Hu, Cong Lu, and Jeff Clune. Their latest research paper introduces a groundbreaking approach to the automatic design of agentic systems, aptly named Automated Design of Agentic Systems (ADAS). This innovative methodology leverages a meta agent to create, test, and refine new agents, pushing the boundaries of what's possible in artificial intelligence (AI).

Key Methodologies: How Does Meta Agent Search Work?

The core of ADAS lies in its ability to automate the design of agentic systems through a meta agent, which essentially acts as an AI architect. The process is encapsulated in an algorithm called Meta Agent Search. Here’s a breakdown of how it works:

1. Meta Agent Programming: Meta Agent Search begins with a meta agent, typically powered by a Foundation Model (FM) like GPT-4, which iteratively programs new agents. The agents are defined in code, leveraging the Turing Completeness of programming languages to explore a vast space of potential agent designs.
2. Iterative Refinement: Each new agent is tested on specific tasks, and its performance is evaluated. Successful agents are added to an "Agent Archive" that the meta agent uses to inform subsequent iterations. This iterative process of programming, testing, and refining enables continuous improvement.
3. Self-Reflection and Error Correction: A unique aspect of the methodology is its use of self-reflection. The meta agent reviews its own designs, identifies errors, and refines the agents based on feedback, ensuring the generation of robust and effective solutions.
4. Cross-Domain and Model Transferability: One of the standout features of agents created by Meta Agent Search is their ability to maintain superior performance across different domains and models. This cross-domain robustness highlights the generality and effectiveness of the discovered agent designs.

Main Findings: Performance Breakthroughs

The research demonstrates that agents designed by Meta Agent Search significantly outperform state-of-the-art hand-designed agents across multiple domains, including coding, science, and math. Here are some key results:

• Reading Comprehension: Discovered agents improved F1 scores by 13.6/100 over existing methods.
• Math Tasks: Accuracy rates saw an uplift of 14.4% in the MGSM benchmark.
• Cross-Domain Transfer: Agents improved accuracy by 25.9% on GSM8K math tasks and 13.2% on GSM-Hard tasks when transferred from the MGSM domain.

These results underscore the potential of ADAS to automate the development of high-performance agentic systems, reducing the need for manual tuning and domain-specific adjustments.

Implications and Applications: Where Can This Take Us?

The implications of ADAS and Meta Agent Search are profound, offering numerous applications across various fields:

1. AI Research and Development: By automating the design of agentic systems, researchers can focus on higher-level challenges, leaving the iterative improvement process to the meta agents. This can accelerate advancements in AI capabilities.
2. Industry Applications: Industries reliant on complex problem-solving, such as finance, healthcare, and engineering, can benefit from tailored agentic systems that adapt and improve over time, enhancing efficiency and decision-making.
3. Education and Training: Adaptive learning systems powered by ADAS can provide personalized educational experiences, adjusting to the needs and progress of individual learners.
4. Robotics and Autonomous Systems: The ability to design agents capable of handling diverse tasks can revolutionize robotics, enabling more sophisticated and adaptable autonomous systems.

Conclusion: A New Dawn in AI Design

The research on ADAS and the Meta Agent Search algorithm marks a significant leap forward in the field of AI. The ability to automatically design and refine agentic systems opens up new avenues for innovation and application, promising to transform how we approach complex problem-solving and system design.

As we stand on the cusp of this new era, the potential for ADAS to reshape industries and advance AI research is immense. The journey has just begun, and the future looks brighter than ever for automated agentic systems.

Quote from the Research: "Our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity."

By embracing the principles of Meta Agent Search, we can look forward to a future where intelligent systems not only assist us but also evolve autonomously, continuously pushing the boundaries of what's possible.

Suggestions for Visuals:

1. Flow Diagram: A visual representation of the Meta Agent Search process, showing the iterative cycle of programming, testing, and refining agents.
2. Performance Graphs: Comparative charts illustrating the performance improvements of discovered agents over state-of-the-art hand-designed agents across various benchmarks.

Stay tuned as the world of AI continues to evolve, and keep an eye out for more breakthroughs in the automated design of agentic systems.