Automated Design of Agentic Systems with Shengran Hu - #700
Digest
This podcast delves into the fascinating world of automated design of agentic systems (ADAS), a research area that utilizes AI to generate algorithms for agents. The guest, Sungran Hu, provides a comprehensive overview of ADAS, highlighting its potential to revolutionize the field of artificial intelligence. The conversation begins by exploring the concept of agentic systems, which are systems that exhibit goal-directed behavior, planning, and interaction with the environment. Hu explains why agentic systems are more powerful than single-query foundation models, drawing an analogy between human task completion and how foundation models can complete tasks. He emphasizes the need for planning, reflection, and external tools for effective problem-solving. The discussion then delves into the relationship between chain of thought prompting and agentic systems. Hu argues that chain of thought techniques are becoming more agentic as they involve planning and iterative interaction with the environment. He further explores the definition of agenticness and the spectrum of agenticness, highlighting the emergence of various building blocks in this space. Hu then discusses the potential of collaborative agentic systems, where agents with different specialties work together. He draws parallels to human organizations and the importance of workflows and standard operating procedures for robust system performance. The conversation also explores the challenges of defining the boundary of an agent, particularly in the context of multi-agent systems. The podcast then shifts to AI generating algorithms (AIGA), a research direction that aims to replace handcrafted designs in AI systems with learned solutions. Hu discusses the evolution of AIGA from early examples in computer vision to more recent advancements in meta-learning. He introduces the concept of automated design of agentic systems (ADAS), which aims to learn from the design itself and meta-learn the design of agentic systems. The conversation explores the trade-off between constraining the search space and implementing constraints in the search algorithm for ADAS. Hu discusses the benefits of both approaches and the importance of providing direction to the search algorithm. He also addresses the question of complexity in ADAS, explaining that the ADAS system can generate complex agents from simple building blocks, and the complexity can co-evolve through iterative design. Hu then discusses the concept of open-ended learning in ADAS and how it allows the system to discover novel building blocks and combine them into more powerful solutions. He highlights the emergence of complex feedback mechanisms in the generated agents, which are combinations of previously discovered stepping stones. The podcast concludes with a discussion about the practical applications of ADAS. Hu acknowledges that the current research is primarily academic but highlights the growing interest from developers in the agent community who are using ADAS in their applications. He emphasizes the potential of ADAS for understanding foundation models better by observing the different agent systems they generate.
Outlines
Introduction to Automated Design of Agentic Systems (ADAS)
This episode explores the automated design of agentic systems (ADAS), a research area focused on using AI to generate algorithms for agents. The guest, Sungran Hu, discusses the motivation behind ADAS, the challenges of designing agentic systems, and the potential benefits of this approach.
The Power of Agentic Systems
Sungran Hu explains the concept of agentic systems and why they are more powerful than single-query foundation models. He draws an analogy between human task completion and how foundation models can complete tasks, highlighting the need for planning, reflection, and external tools for effective problem-solving.
Chain of Thought and Agenticness
The conversation explores the relationship between chain of thought prompting and agentic systems. Sungran Hu argues that chain of thought techniques are becoming more agentic as they involve planning and iterative interaction with the environment.
Defining Agenticness and Collaborative Systems
The discussion delves into the definition of agenticness and whether there is a clear threshold. Sungran Hu emphasizes the spectrum of agenticness and the emergence of various building blocks in this space. He also discusses the potential of collaborative agentic systems, where agents with different specialties work together, drawing parallels to human organizations and the importance of workflows and standard operating procedures for robust system performance.
The Boundary of an Agent and AI Generating Algorithms (AIGA)
The conversation explores the challenges of defining the boundary of an agent, particularly in the context of multi-agent systems. Sungran Hu highlights the need for a clear definition of modules and the potential for diverse perspectives to enhance system robustness. He then provides an overview of AI generating algorithms (AIGA), a research direction that aims to replace handcrafted designs in AI systems with learned solutions. He discusses the evolution of AIGA from early examples in computer vision to more recent advancements in meta-learning.
Automated Design of Agentic Systems (ADAS) and its Components
Sungran Hu introduces the concept of automated design of agentic systems (ADAS), which aims to learn from the design itself and meta-learn the design of agentic systems. He outlines the three key components of ADAS: search space, search algorithm, and evaluation function.
Constraining the Search Space and Complexity in ADAS
The conversation explores the trade-off between constraining the search space and implementing constraints in the search algorithm for ADAS. Sungran Hu discusses the benefits of both approaches and the importance of providing direction to the search algorithm. He also addresses the question of complexity in ADAS, explaining that the ADAS system can generate complex agents from simple building blocks, and the complexity can co-evolve through iterative design.
Open-Ended Learning and Emergent Behaviors in ADAS
Sungran Hu discusses the concept of open-ended learning in ADAS and how it allows the system to discover novel building blocks and combine them into more powerful solutions. He highlights the emergence of complex feedback mechanisms in the generated agents, which are combinations of previously discovered stepping stones.
Understanding Foundation Models through ADAS
Sungran Hu explores the potential of ADAS for understanding foundation models. He suggests that by observing the different agent systems generated by ADAS using different foundation models, we can gain insights into the strengths and weaknesses of those models.
Practical Applications and Future of ADAS
The conversation concludes with a discussion about the practical applications of ADAS. Sungran Hu acknowledges that the current research is primarily academic but highlights the growing interest from developers in the agent community who are using ADAS in their applications. He emphasizes the potential of ADAS for designing agents for specific applications and for continuously improving existing agent systems.
Keywords
Agentic Systems
Systems that exhibit goal-directed behavior, planning, and interaction with the environment. They are more powerful than single-query foundation models because they can reason, reflect, and use external tools.
Chain of Thought Prompting
A technique that involves prompting LLMs to reason and plan before producing a final response. It is becoming more agentic as it involves iterative interaction with the environment.
Automated Design of Agentic Systems (ADAS)
A research area focused on using AI to generate algorithms for agents. It aims to learn from the design itself and meta-learn the design of agentic systems.
AI Generating Algorithms (AIGA)
A research direction that aims to replace handcrafted designs in AI systems with learned solutions. It has evolved from early examples in computer vision to more recent advancements in meta-learning.
Open-Ended Learning
A learning paradigm where the system is allowed to explore a vast search space and discover novel building blocks. It enables the emergence of complex behaviors and solutions.
Exploration-Exploitation Trade-off
A fundamental challenge in optimization where the system must balance exploring new possibilities with exploiting existing knowledge to find better solutions.
Meta-Agent
An agent that is designed to program or invent new agents. It leverages the knowledge and expertise of foundation models in coding and agent systems.
Multi-Objective Optimization
An optimization approach that aims to optimize multiple objectives simultaneously. It results in a Pareto front, which represents a trade-off between different objectives.
Q&A
What are the key components of ADAS?
ADAS has three key components: search space, search algorithm, and evaluation function. The search space defines the possible designs for agentic systems, the search algorithm explores this space, and the evaluation function assesses the quality of the generated solutions.
How does ADAS address the challenge of designing complex agentic systems?
ADAS uses a meta-agent to program and invent new agents in code. This approach leverages the foundation model's expertise in coding and agent systems, allowing it to generate novel and effective designs efficiently.
What are some potential benefits of using ADAS?
ADAS can help us understand foundation models better by observing the different agent systems they generate. It can also help us explore the evolution of complexity in systems, potentially leading to insights into how human organizations and societies evolve.
How close is ADAS to practical application?
While the current research is primarily academic, there is growing interest from developers in the agent community who are using ADAS in their applications. ADAS has the potential to be used for designing agents for specific applications and for continuously improving existing agent systems.
What are some challenges and opportunities associated with using ADAS for continuous improvement of agent systems?
Continuous improvement of agent systems using ADAS presents challenges such as stability risks and the need to vet new code. However, it also offers opportunities for closing the loop with data and continuously improving system performance.
Show Notes
Today, we're joined by Shengran Hu, a PhD student at the University of British Columbia, to discuss Automated Design of Agentic Systems (ADAS), an approach focused on automatically creating agentic system designs. We explore the spectrum of agentic behaviors, the motivation for learning all aspects of agentic system design, the key components of the ADAS approach, and how it uses LLMs to design novel agent architectures in code. We also cover the iterative process of ADAS, its potential to shed light on the behavior of foundation models, the higher-level meta-behaviors that emerge in agentic systems, and how ADAS uncovers novel design patterns through emergent behaviors, particularly in complex tasks like the ARC challenge. Finally, we touch on the practical applications of ADAS and its potential use in system optimization for real-world tasks.
The complete show notes for this episode can be found at https://twimlai.com/go/700.
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