Agentic Spectrum

Intro

Who is deciding how to construct and traverse the execution graph of an AI agent?

Why is this important?

Spectrum

• L1 agents are traditional computer programs and deterministic workflows.
• The execution graph is controlled by static code which is most often written by a human programmer.
• L1 agents may invoke LLMs or other AI models, but they do not rely on these models to determine the program’s control flow.
• L1 agents may be autonomous in that they don’t require user interaction at runtime (like a background workflow script), but they are not autonomous from the perspective of the human programmers who created them. E.g., the human programmer is still driving the bus.

• L2 agents use LLMs selectively to decide how to handle key points in the program’s control flow.
• Today, this often boils down to deciding which tool to invoke based on a set of tools which have been carefully curated by a human programmer.
• The most common example of L2 agents today is invoking an LLM with access to tools in a while loop.
• The majority of the program’s control flow still resides outside of the LLM’s purview and is controlled by a human programmer.

• L3 agents are defined by an execution graph that is constructed statically by an expert human programmer which is handed off to an LLM to determine how to traverse this graph at runtime.
• The defining characteristic of L3 agents is that they tend to be hierarchical or recursive in nature, but the entire execution graph has been carefully crafted by a human programmer ahead of time.
• L3 agents are commonly composed of L1/L2 agents with some higher-order agent orchestrating the execution of these sub-agents.

• L4 agents add some dynamic actions to the execution graph which are not known a priori by the human programmer.
• The majority of the control flow may still controlled by code written by a human programmer, but for the first time, L4 agents introduce the ability for an LLM to create novel, dynamic actions at runtime.
• If an agent creates and executes dynamic code at runtime, it is at least an L4 agent.

• L5 agents are fully autonomous programs where the entire execution graph is constructed and traversed on-the-fly.
• Examples of prototypical L5 agents include BabyAGI, AutoGPT, and MultiOn.

Insights

• You can reduce L4/L5 agents to the dynamic construction of an execution graph plus the ability to execute that graph reliably – e.g., L3 agents.

• L4 and L5 agents are really the only point at which cognitive architectures come into play.

• Reliable, general L5 agents are functionally equivalent to AGI.

• The same spectrum can be applied to embodied agents aka robots.

• The L1-L5 levels of self-driving vehicle automation are a very useful analog when thinking about the agentic spectrum.

Further Reading

What is an AI agent?

Learn what AI agents are, how they work in an LLM loop, and where workflows fit so you can build reliable, production-ready autonomous systems.

https://www.langchain.com/blog/what-is-an-agent

LLM Powered Autonomous Agents

Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver. Agent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components: Planning Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks. Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results. Memory Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn. Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval. Tool use The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more. Overview of a LLM-powered autonomous agent system. Component One: Planning A complicated task usually involves many steps. An agent needs to know what they are and plan ahead.

https://lilianweng.github.io/posts/2023-06-23-agent/

An Army of Agents

An approachable introduction to AI agents and why they’re likely to be the most impactful technology of our generation.

https://transitivebullsh.it/ai-agents