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Agent System Overview

In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components.

Planning: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.

Memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.

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.

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Let's talk about the Agent System Overview. Large Language Models or LLMs, acts like the brain of an autonomous agent. This system is made up of several key parts.

First up, we have Planning. Here's how it works: the agent takes big tasks and breaks them down into smaller, more manageable goals. This makes it easier to handle complex tasks.

Next, we have Memory. Think of this as the agent using its short-term memory to learn from what's happening around it. This is also known as in-context learning.

Finally, we have Tool use. This is where the agent learns to use external sources, or APIs, to get extra information that it doesn't already have from pre-training. This could be anything from current information, the ability to execute code, or even access to exclusive information sources and more.