An AI Agent is a software system that uses an AI model (usually an LLM) to understand a goal, decide what actions to take, use tools, and work toward completing a task.

Unlike a normal chatbot that primarily responds to questions, an AI Agent can take actions using APIs, databases, browsers, or other software systems.

1. Chatbot vs AI Agent

Chatbot	AI Agent
Answers questions	Works toward completing goals
Primarily generates text	Generates text and performs actions
Usually responds to each prompt	Can execute multiple steps
Limited tool usage	Can use tools and APIs
Example: Explain an invoice	Example: Find unpaid invoices and prepare follow-ups

An AI Agent typically works like this:

1. Goal: User gives the agent a task.
2. Reasoning: LLM analyzes the task and decides what to do.
3. Planning: Agent determines the sequence of actions.
4. Tool execution: Agent calls APIs, databases, or other tools.
5. Observation: Agent checks the results.
6. Iteration: It decides whether another action is required.
7. Completion: Agent returns the result or completes the task.

The process may repeat several times until the goal is achieved or the agent needs human input.

3. Example: Banking AI Agent

Imagine creating an AI Agent for a banking application.

User asks:

Find my last 5 transactions and identify unusual payments.

The agent could perform:

User Request

LLM – Understand the request

Determines that transaction data must be retrieved.

Tool – Transaction API

Calls banking API to retrieve transactions.

LLM – Analyze transactions

Evaluates transaction details and potential anomalies.

Final Response

Explains findings to the user.

The AI Agent does not directly access the banking database by itself. It uses tools provided by the application, subject to authorization and security controls.

4. AI Agent Architecture

Typical components:

Component	Responsibility
LLM	Reasoning and decision-making
Tools	APIs, database queries, functions
Memory	Retains relevant context
Planner	Determines steps to execute
Execution engine	Runs tools and manages workflow
Guardrails	Controls permissions and unsafe actions

Not every AI Agent requires a separate planner or memory database. Simple agents can use an LLM with a few tools.

5. How to build an AI Agent using Java Spring Boot?

You can use Spring AI to build AI Agents.

Relevant technologies:

• Spring AI – AI integration framework for Spring Boot.
• Ollama – Run local LLMs.
• PostgreSQL + pgvector – Store embeddings and retrieve knowledge.
• REST APIs – Connect agents to business systems.
• MCP (Model Context Protocol) – Standardized way to expose tools and context to AI applications.

A basic architecture:

User
|
v
Spring Boot AI Agent
|
+—- LLM (Ollama / GPT)
|
+—- Banking APIs
|
+—- Database Tools
|
+—- Vector Database
|
v
Task Execution and Response

6. Difference between RAG and AI Agent

RAG	AI Agent
Retrieves relevant information	Performs tasks
Uses knowledge documents	Uses tools and APIs
Provides context to LLM	Can decide which tools to invoke
Mainly knowledge-based	Action-oriented

An AI Agent can also use RAG.

For example, a Backbase development agent could retrieve architecture documentation through RAG, inspect configuration files, analyze errors, and propose changes.

Final understanding

AI ecosystem in one line:

LLM = Intelligence

Embedding Model = Semantic representation

Vector Database = Knowledge retrieval

RAG = Knowledge grounding

AI Agent = Intelligence + Tools + Actions