Self-Designed AI: Introducing Automated Agent Creation
Accelerating AI Evolution and Autonomy
We’re living in the age of incredibly powerful Large Language Models (LLMs), but even the most sophisticated LLMs need structure and guidance to reliably solve complex problems. That’s where agentic systems come in. Think of them as frameworks built around LLMs, incorporating things like planning, tool use, and self-reflection to take the rights actions and achieve your goal.
Up until now, building these agentic systems has been a painstaking, manual process. Researchers and engineers have had to meticulously hand-craft each component, experiment with different combinations, and rigorously configure for specific tasks. It’s a time-consuming bottleneck in the development of truly powerful LLM-based agents.
But what if we could automate this design process? What if we could let AI design the AI? That’s the audacious goal of a new research area called Automated Design of Agentic Systems (ADAS).
How ADAS Works: AI Coding AI
The key insight behind ADAS is to use code as the design language for agentic systems. This leverages a few powerful ideas:
Turing Completeness: Programming languages are “Turing Complete,” meaning they can theoretically represent any computational process – including the intricate designs of agentic systems.
LLM Coding Proficiency: Modern LLMs are becoming increasingly adept at writing and understanding code, making them ideal candidates for automating agent design.
Imagine a “meta agent” – an automated LLM-based process specifically designed to identify and create new agents. It iteratively creates agents in code, tests them on specific tasks, learns from the results, and stores successful designs in an “archive” for future inspiration. This process, called Meta Agent Search, mimics the way human researchers iterate and build upon previous discoveries. Check out their GitHub repo and see how it works for yourself.
The Surprising Results: Learned Agents Outshine Hand-Designed Ones
The early results of ADAS are remarkable. In experiments across various domains, including logic puzzles, reading comprehension, math, and even multi-task problem solving, learned agents consistently outperform state-of-the-art hand-designed agents.
Even more surprisingly, these learned agents show a remarkable ability to generalize. One striking example is how an agent initially designed for solving complex math problems was able to transfer to reading comprehension tasks, maintaining competitive performance. This cross-domain generalization highlights the robustness of the agent designs uncovered by ADAS. An agent designed to solve math problems can be transferred to reading comprehension tasks and still achieve competitive performance. This suggests that ADAS is uncovering fundamental design patterns that transcend individual domains.
Implications and The Future
The research into ADAS is just beginning, but it holds the promise of turbo-charging how we create and deploy LLM-based agents. It’s a powerful example of how AI can not only solve problems but also design the solutions to those problems – a glimpse into a future where AI systems become increasingly self-sufficient and capable of shaping their own evolution.