Autonomous AI Agents – More Than Just a Chatbot

In the new wave of artificial intelligence, AI agents are emerging as the systems that deliver real, practical value. Unlike a chatbot, which only responds to one request at a time, AI agents can reason and act autonomously to achieve defined goals.

Agents represent a shift from Generative AI (which helps create content) to Agentic AI (which can act on your behalf).

The Core of an AI Agent: Five Key Components

To achieve autonomy, all AI agents follow a fundamental pattern involving continuous planning, execution, and feedback. This pattern consists of:

1. Goal: The desired state the agent aims to achieve—for example, maximizing crop yield in agriculture.

2. Planner: Uses a large language model (LLM) to break down complex tasks into subtasks and determine the order in which they should be carried out.

3. Memory: The agent’s knowledge store. It includes short-term memory (to remember the current context) and long-term memory (which uses vector databases to store domain-specific facts and user preferences over time).

4. Executor: The component that carries out actions based on the plan.

5. Action: The interaction with the external world, often through external tools and IoT controllers.

RAG: The Agent’s True Knowledge Base

An LLM-based agent cannot store all of a company’s knowledge or real-time data. This is why Retrieval-Augmented Generation (RAG) is one of the three most crucial optimization techniques for AI agents.

RAG enables the agent to:

• Retrieve Accurate Facts: When writing a blog post, the agent can use RAG to fetch the latest statistics or relevant research articles from a vector database, ensuring that the content is factually grounded rather than based on outdated LLM training data.

• Access Proprietary Information: For questions about company policies or customer history, the query is converted into a vector and searched in a vector database (containing company documents divided into chunks). The most relevant content is then added to the agent’s prompt as context.

• Support the Agent’s Autonomy: Without RAG, the agent is limited to static knowledge. With RAG, the agent gains dynamic access to knowledge sources as needed.