Components of Autonomous Agents
We deploy agents to automate tasks using Large Language Models (LLMs). Agents enable LLMs to act autonomously. They execute tasks with minimal human input.
Here’s how they work:
An agent comprises four core components:
- Planning Module: It orchestrates tasks. This module determines what tasks the LLM needs to perform and in what sequence.
- Memory/Storage: Data needed for tasks is stored here. This could be a vector database or prompt-based short-term memory.
- Tools: These are third-party APIs or workflows the agent uses to perform actions. Examples include sending requests, publishing content, or interacting with databases.
- LLM Core: The engine of the agent. This could be an API or a locally hosted LLM. It processes information and produces outputs.
Example Application:
Suppose we build a content generation agent. It generates articles, then autonomously posts them on social media. The architecture might look like this:
- The article is stored in an SQL database.
- Summaries are created by passing the article to the LLM as a context.
- APIs publish these summaries on social platforms.
This setup saves time and automates content management. However, validation workflows, such as fact-checking, are still needed. As we evolve, agents can be chained, forming workflows capable of handling more complex operations autonomously- which is being done with frameworks like langgraph.
