Building AI-powered applications has become increasingly important for modern Java developers. With the rise of large language models and AI services, integrating intelligent capabilities into Java applications is no longer a luxury — it's a necessity for staying competitive.
Spring AI makes this integration seamless by providing a unified framework for building AI-powered applications with Java. Combined with Amazon Bedrock, developers can create sophisticated AI agents that leverage state-of-the-art foundation models without managing complex infrastructure.
In this post, I'll guide you through creating your first AI agent using Java and Spring AI, connected to Amazon Bedrock. We'll build a complete application with both REST API and web interface, demonstrating how to integrate AI capabilities into your Java applications.
Overview of the Solution
What is Spring AI?
Spring AI is a framework that brings the familiar Spring programming model to AI applications. It provides:
Unified API: A consistent interface for working with different AI models and providers
Seamless Integration: Native Spring Boot integration with auto-configuration
Multiple Providers: Support for OpenAI, Azure OpenAI, Amazon Bedrock, Ollama, and more
Advanced Features: Built-in support for RAG (Retrieval Augmented Generation), function calling, and chat memory
Production Ready: Observability, error handling, and resilience patterns
The framework abstracts the complexity of working with different AI providers, allowing you to focus on building your application logic rather than dealing with API specifics.
What is Amazon Bedrock?
Amazon Bedrock is a fully managed service that provides access to high-performing foundation models from leading AI companies including Anthropic, Meta, Stability AI, and Amazon's own models. Key benefits include:
No Infrastructure Management: Serverless architecture with automatic scaling
Multiple Models: Access to various foundation models through a single API
Security & Privacy: Your data stays within your AWS account
Cost Effective: Pay only for what you use with no upfront commitments
We'll create a Spring Boot application that demonstrates the core concepts of building AI agents.
The application will include:
- Spring Boot web application with Thymeleaf templates
- REST API for chat interactions
- Integration with Amazon Bedrock via Spring AI
- Streaming responses for better user experience
Prerequisites
Before you start, ensure you have:
- Java 21 JDK installed (we'll use Amazon Corretto 21)
- Maven 3.6+ installed
- AWS CLI configured with appropriate permissions for Amazon Bedrock
- Access to Amazon Bedrock models in your AWS account
Creating the AI Agent
Use Spring Initializer
We'll use Spring Initializer to bootstrap our project with the necessary dependencies:
curl https://start.spring.io/starter.zip \
-d type=maven-project \
-d language=java \
-d bootVersion=3.5.7 \
-d baseDir=ai-agent \
-d groupId=com.example \
-d artifactId=ai-agent \
-d name=ai-agent \
-d description="AI Agent with Spring AI and Amazon Bedrock" \
-d packageName=com.example.ai.agent \
-d packaging=jar \
-d javaVersion=21 \
-d dependencies=spring-ai-bedrock-converse,web,thymeleaf,actuator,devtools \
-o ai-agent.zip
unzip ai-agent.zip
cd ai-agent
Add a Git repository to the project to track changes
git init -b main
git add .
git commit -m "Initialize the AI agent"
Configure Amazon Bedrock Integration
Create the application configuration in src/main/resources/application.properties:
cat >> src/main/resources/application.properties << 'EOL'
# Simplified logging pattern - only show the message
logging.pattern.console=%msg%n
# Debugging
logging.level.org.springframework.ai=DEBUG
spring.ai.chat.observations.log-completion=true
spring.ai.chat.observations.include-error-logging=true
spring.ai.tools.observations.include-content=true
# Thymeleaf Configuration
spring.thymeleaf.cache=false
spring.thymeleaf.prefix=classpath:/templates/
spring.thymeleaf.suffix=.html
# Amazon Bedrock Configuration
spring.ai.bedrock.aws.region=us-east-1
spring.ai.bedrock.converse.chat.options.max-tokens=10000
spring.ai.bedrock.converse.chat.options.model=openai.gpt-oss-120b-1:0
EOL
The most important part of the properties is the GenAI model which we are going to use.
In this example, we use OpenAI’s open-weight model gpt-oss-120b.
This model is offered on Amazon Bedrock and provides an excellent cost/performance ratio and can be recommended as a migration path from proprietary OpenAI models.
Create the Chat Service
The ChatClient interface is the core abstraction in Spring AI that provides:
Unified API: Works with different AI model providers without code changes
Streaming Support: Real-time response streaming for better user experience
Prompt Management: Built-in support for structured prompts and templates
Response Handling: Consistent response format across different models
Key methods include:
call(Prompt prompt): Synchronous response
stream(Prompt prompt): Streaming response
generate(List<Prompt> prompts): Batch processing
Create a service to handle chat interactions with the AI model:
mkdir -p src/main/java/com/example/ai/agent/service
cat <<'EOF' > src/main/java/com/example/ai/agent/service/ChatService.java
package com.example.ai.agent.service;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.stereotype.Service;
import org.springframework.ai.chat.client.ChatClient;
@Service
public class ChatService {
private static final Logger logger = LoggerFactory.getLogger(ChatService.class);
private final ChatClient chatClient;
public ChatService(ChatClient.Builder chatClientBuilder) {
this.chatClient = chatClientBuilder
.build();
logger.info("ChatService initialized with embedded ChatClient");
}
public String processChat(String prompt) {
logger.info("Processing text chat request - prompt: '{}'", prompt);
try {
var chatResponse = chatClient
.prompt().user(prompt)
.call()
.chatResponse();
return extractTextFromResponse(chatResponse);
} catch (Exception e) {
logger.error("Error processing chat request", e);
return "I don't know - there was an error processing your request.";
}
}
public String extractTextFromResponse(org.springframework.ai.chat.model.ChatResponse chatResponse) {
if (chatResponse != null) {
// First try the standard approach
String text = chatResponse.getResult().getOutput().getText();
if (text != null && !text.isEmpty()) {
return text;
}
// Fallback: iterate through generations for models with reasoning content
if (!chatResponse.getResults().isEmpty()) {
for (var generation : chatResponse.getResults()) {
String textContent = generation.getOutput().getText();
if (textContent != null && !textContent.isEmpty()) {
logger.info("Found text content: '{}'", textContent);
return textContent;
}
}
}
}
return "I don't know - no response received.";
}
}
EOF
Create the Chat Controller
Create the REST controller that handles chat interactions:
mkdir -p src/main/java/com/example/ai/agent/controller
cat <<'EOF' > src/main/java/com/example/ai/agent/controller/ChatController.java
package com.example.ai.agent.controller;
import com.example.ai.agent.service.ChatService;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RestController;
@RestController
@RequestMapping("api")
public class ChatController {
private final ChatService chatService;
public ChatController(ChatService chatService) {
this.chatService = chatService;
}
@PostMapping("chat")
public String chat(@RequestBody ChatRequest request) {
return chatService.processChat(request.prompt());
}
public record ChatRequest(String prompt) {}
}
EOF
Create the Web Controller
Create a controller to serve the web interface:
cat <<'EOF' > src/main/java/com/example/ai/agent/controller/WebViewController.java
package com.example.ai.agent.controller;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.GetMapping;
@Controller
public class WebViewController {
@GetMapping("/")
public String index() {
return "chat";
}
}
EOF
Create the Web Interface
Create a modern chat interface using Thymeleaf and Tailwind CSS:
cat <<'EOF' > src/main/resources/templates/chat.html
<!DOCTYPE html>
<html xmlns:th="http://www.thymeleaf.org">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Agent</title>
<script src="https://cdn.tailwindcss.com"></script>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/marked.min.js"></script>
<script>
tailwind.config = {
darkMode: 'class',
theme: {
extend: {
colors: {
'dev-dark': '#1a1a1a',
'dev-darker': '#121212',
'dev-accent': '#6366f1',
'dev-accent-hover': '#4f46e5',
'dev-secondary': '#3b82f6',
'dev-text': '#e2e8f0',
'dev-text-muted': '#94a3b8',
'dev-border': '#2d3748',
'dev-message-user': '#1e293b',
'dev-message-ai': '#1e1e2d',
// Light theme colors
'light-bg': '#ffffff',
'light-bg-secondary': '#f8fafc',
'light-text': '#1f2937',
'light-text-muted': '#6b7280',
'light-border': '#e5e7eb',
'light-message-user': '#f3f4f6',
'light-message-ai': '#ffffff'
}
}
}
}
</script>
<style>
.chat-container {
height: calc(100vh - 180px);
}
.message-container {
height: calc(100vh - 280px);
overflow-y: auto;
}
/* Custom scrollbar for dark mode */
.dark ::-webkit-scrollbar {
width: 8px;
}
.dark ::-webkit-scrollbar-track {
background: #1a1a1a;
}
.dark ::-webkit-scrollbar-thumb {
background: #3b3b3b;
border-radius: 4px;
}
.dark ::-webkit-scrollbar-thumb:hover {
background: #4b4b4b;
}
/* Light theme scrollbar */
::-webkit-scrollbar {
width: 8px;
}
::-webkit-scrollbar-track {
background: #f1f5f9;
}
::-webkit-scrollbar-thumb {
background: #cbd5e1;
border-radius: 4px;
}
::-webkit-scrollbar-thumb:hover {
background: #94a3b8;
}
/* Markdown table styling */
.ai-response table {
width: 100%;
border-collapse: collapse;
margin: 1rem 0;
border-radius: 8px;
overflow: hidden;
background: #ffffff;
border: 1px solid #e5e7eb;
}
.dark .ai-response table {
background: #1e1e2d !important;
border: 1px solid #2d3748 !important;
}
.ai-response th {
background: #6366f1 !important;
color: white !important;
padding: 12px;
text-align: left;
font-weight: 600;
}
.ai-response td {
padding: 10px 12px;
border-bottom: 1px solid #e5e7eb;
color: #111827 !important;
background: #ffffff !important;
font-weight: 600 !important;
}
.dark .ai-response td {
border-bottom: 1px solid #2d3748 !important;
color: #f9fafb !important;
background: #1e1e2d !important;
}
.ai-response tr:last-child td {
border-bottom: none;
}
.ai-response tr:nth-child(even) td {
background: #f1f5f9 !important;
color: #111827 !important;
}
.dark .ai-response tr:nth-child(even) td {
background: #2a2a3a !important;
color: #f9fafb !important;
}
.ai-response h1, .ai-response h2, .ai-response h3 {
color: #6366f1;
margin: 1rem 0 0.5rem 0;
}
.ai-response strong {
color: #111827 !important;
font-weight: 700 !important;
}
.dark .ai-response strong {
color: #f9fafb !important;
font-weight: 700 !important;
}
.ai-response em {
color: #6b7280;
}
.dark .ai-response em {
color: #94a3b8;
}
.ai-response p {
color: #1f2937;
}
.dark .ai-response p {
color: #e2e8f0;
}
/* Message bubble backgrounds */
.message-bubble-ai {
background: #ffffff;
border: 1px solid #e5e7eb;
color: #1f2937;
}
.dark .message-bubble-ai {
background: #1e1e2d !important;
border: 1px solid #2d3748 !important;
color: #e2e8f0 !important;
}
.message-bubble-user {
background: #f3f4f6;
border: 1px solid #e5e7eb;
color: #1f2937;
}
.dark .message-bubble-user {
background: #1e293b !important;
border: 1px solid #2d3748 !important;
color: #e2e8f0 !important;
}
/* Text inside message bubbles */
.message-bubble-ai p, .message-bubble-ai * {
color: #1f2937 !important;
}
.dark .message-bubble-ai p, .dark .message-bubble-ai * {
color: #e2e8f0 !important;
}
.message-bubble-user p, .message-bubble-user * {
color: #1f2937 !important;
}
.dark .message-bubble-user p, .dark .message-bubble-user * {
color: #e2e8f0 !important;
}
.image-modal {
display: none;
position: fixed;
z-index: 1000;
left: 0;
top: 0;
width: 100%;
height: 100%;
background-color: rgba(0, 0, 0, 0.9);
}
.image-modal img {
margin: auto;
display: block;
max-width: 90%;
max-height: 90%;
margin-top: 5%;
}
.image-modal .close {
position: absolute;
top: 15px;
right: 35px;
color: #f1f1f1;
font-size: 40px;
font-weight: bold;
cursor: pointer;
}
.image-modal .close:hover {
color: #bbb;
}
/* Copy button styling */
.copy-button {
position: absolute;
top: 8px;
right: 8px;
background: rgba(99, 102, 241, 0.8);
color: white;
border: none;
border-radius: 4px;
padding: 4px 8px;
font-size: 12px;
cursor: pointer;
opacity: 0;
transition: opacity 0.2s;
}
.copy-button:hover {
background: rgba(99, 102, 241, 1);
}
.message-bubble-ai:hover .copy-button,
.message-bubble-user:hover .copy-button {
opacity: 1;
}
.message-bubble-ai,
.message-bubble-user {
position: relative;
}
</style>
</head>
<body class="bg-light-bg dark:bg-dev-darker min-h-screen text-light-text dark:text-dev-text transition-colors duration-300">
<div class="container mx-auto px-4 py-8">
<!-- Header -->
<header class="mb-8">
<div class="flex justify-between items-center">
<div class="flex items-center">
<h1 class="text-3xl font-bold text-dev-accent">🤖 AI Agent</h1>
</div>
<button id="themeToggle" class="bg-dev-accent text-white px-4 py-2 rounded hover:bg-dev-accent-hover transition">
🌙 Dark
</button>
</div>
<p class="text-light-text-muted dark:text-dev-text-muted mt-2">Chat with our AI Agent to help you with your questions!</p>
</header>
<!-- Chat Interface -->
<div class="bg-light-bg-secondary dark:bg-dev-dark rounded-xl shadow-lg p-6 chat-container border border-light-border dark:border-dev-border flex flex-col">
<div class="message-container flex-grow mb-4 bg-light-bg dark:bg-dev-darker rounded-lg p-4" id="messageContainer">
<div class="flex mb-4">
<div class="w-10 h-10 rounded-full bg-dev-accent bg-opacity-20 flex items-center justify-center mr-3">
<span class="text-lg">🤖</span>
</div>
<div class="message-bubble-ai rounded-lg p-3 max-w-3xl">
<p>Welcome to the AI Agent! How can I help you today?</p>
</div>
</div>
<!-- Messages will be added here dynamically -->
</div>
<!-- Input Area -->
<div class="border-t border-light-border dark:border-dev-border pt-4 mt-auto">
<form id="chatForm" class="flex w-full">
<input type="file" id="fileInput" class="hidden" accept=".jpg,.jpeg,.png">
<button type="button" id="fileButton" class="bg-dev-accent text-white px-4 py-3 rounded-l-lg hover:bg-dev-accent-hover transition flex items-center justify-center">
📎
</button>
<input type="text" id="userInput" class="flex-grow bg-light-bg dark:bg-dev-darker border border-light-border dark:border-dev-border px-4 py-3 focus:outline-none focus:ring-2 focus:ring-dev-accent text-light-text dark:text-dev-text" placeholder="Please ask your question...">
<button type="submit" class="bg-dev-accent text-white px-6 py-3 rounded-r-lg hover:bg-dev-accent-hover transition">Send</button>
</form>
</div>
</div>
</div>
<!-- Image zoom modal -->
<div id="imageModal" class="image-modal">
<span class="close">×</span>
<img id="modalImage" src="" alt="">
</div>
<script>
document.addEventListener('DOMContentLoaded', function() {
// Configure marked.js for tables
marked.setOptions({
gfm: true,
tables: true,
breaks: true
});
const messageContainer = document.getElementById('messageContainer');
const chatForm = document.getElementById('chatForm');
const userInput = document.getElementById('userInput');
const fileInput = document.getElementById('fileInput');
const fileButton = document.getElementById('fileButton');
// File upload button click handler
fileButton.addEventListener('click', function() {
fileInput.click();
});
let selectedFile = null;
let currentFileBase64 = null;
let currentFileName = null;
// File selection handler
fileInput.addEventListener('change', async function(e) {
const file = e.target.files[0];
if (file) {
try {
// Convert file to base64 and store locally
currentFileBase64 = await fileToBase64(file);
currentFileName = file.name;
// Show file attached message with image preview
addImageMessage(file);
console.log('File processed successfully, base64 length:', currentFileBase64.length);
// Automatically analyze the document by sending empty prompt
await sendDocumentAnalysis();
} catch (error) {
console.error('Error processing file:', error);
addMessage('Error processing file. Please try again.', 'ai');
}
}
});
// Function to add image message with preview
function addImageMessage(file) {
const messageDiv = document.createElement('div');
messageDiv.className = 'flex mb-4';
// Create image preview URL
const imageUrl = URL.createObjectURL(file);
messageDiv.innerHTML = `
<div class="ml-auto flex">
<div class="message-bubble-user rounded-lg p-3 max-w-3xl">
<button class="copy-button" data-copy-text="File attached: ${file.name}">📋</button>
<p class="text-light-text dark:text-dev-text mb-2">📎 File attached: ${file.name}</p>
<img src="${imageUrl}" alt="${file.name}" class="max-w-full max-h-64 rounded border border-light-border dark:border-dev-border cursor-pointer hover:opacity-80 transition" onclick="openImageModal('${imageUrl}')" />
</div>
<div class="w-10 h-10 rounded-full bg-dev-secondary bg-opacity-20 flex items-center justify-center ml-3">
<span class="text-lg">👤</span>
</div>
</div>
`;
messageContainer.appendChild(messageDiv);
messageContainer.scrollTop = messageContainer.scrollHeight;
}
// Copy to clipboard function
function copyToClipboard(text) {
navigator.clipboard.writeText(text).then(function() {
console.log('Text copied to clipboard');
}).catch(function(err) {
console.error('Failed to copy text: ', err);
});
}
// Event delegation for copy buttons
messageContainer.addEventListener('click', function(e) {
if (e.target.classList.contains('copy-button')) {
const textToCopy = e.target.getAttribute('data-copy-text');
copyToClipboard(textToCopy);
}
});
// Function to send document analysis with empty prompt and clear file after
async function sendDocumentAnalysis() {
try {
// Show loading indicator
const loadingId = showLoading();
console.log('Sending document analysis with empty prompt');
const response = await fetch('api/chat', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
prompt: "", // Empty prompt - server will use default analysis prompt
fileBase64: currentFileBase64,
fileName: currentFileName
})
});
// Remove loading indicator
removeLoading(loadingId);
if (!response.ok) {
throw new Error('Failed to analyze document');
}
const analysisResult = await response.text();
addMessage(analysisResult, 'ai');
// Clear file data so subsequent chats are text-only
currentFileBase64 = null;
currentFileName = null;
fileInput.value = ''; // Clear file input
} catch (error) {
console.error('Error analyzing document:', error);
addMessage('I don\'t know - there was an error analyzing the document.', 'ai');
// Clear file data even on error
currentFileBase64 = null;
currentFileName = null;
fileInput.value = '';
}
};
chatForm.addEventListener('submit', async function(e) {
e.preventDefault();
const message = userInput.value.trim();
if (!message) return;
// Add user message to chat
addMessage(message, 'user');
userInput.value = '';
try {
// Show loading indicator
const loadingId = showLoading();
// Always use the unified chat endpoint
console.log('Sending request to unified endpoint');
if (currentFileBase64) {
console.log('Including file data:', currentFileName, 'Base64 length:', currentFileBase64.length);
}
const response = await fetch('api/chat', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({
prompt: message,
fileBase64: currentFileBase64 || null,
fileName: currentFileName || null
})
});
if (!response.ok) {
throw new Error('Failed to get response');
}
const data = await response.text();
// Remove loading indicator
removeLoading(loadingId);
// Add AI response to chat
addMessage(data, 'ai');
} catch (error) {
console.error('Error:', error);
addMessage('Sorry, I encountered an error. Please try again.', 'ai');
}
});
function addMessage(content, sender) {
const messageDiv = document.createElement('div');
messageDiv.className = 'flex mb-4';
if (sender === 'user') {
messageDiv.innerHTML = `
<div class="ml-auto flex">
<div class="message-bubble-user rounded-lg p-3 max-w-3xl">
<button class="copy-button" data-copy-text="${escapeHtml(content)}">📋</button>
<p class="text-light-text dark:text-dev-text">${escapeHtml(content)}</p>
</div>
<div class="w-10 h-10 rounded-full bg-dev-secondary bg-opacity-20 flex items-center justify-center ml-3">
<span class="text-lg">👤</span>
</div>
</div>
`;
} else {
// Always parse AI responses as markdown
console.log('Parsing AI response as markdown:', content.substring(0, 200));
const renderedContent = marked.parse(content);
console.log('Rendered HTML:', renderedContent.substring(0, 200));
messageDiv.innerHTML = `
<div class="w-10 h-10 rounded-full bg-dev-accent bg-opacity-20 flex items-center justify-center mr-3">
<span class="text-lg">🤖</span>
</div>
<div class="message-bubble-ai rounded-lg p-3 max-w-4xl ai-response">
<button class="copy-button" data-copy-text="${escapeHtml(content)}">📋</button>
${renderedContent}
</div>
`;
}
messageContainer.appendChild(messageDiv);
messageContainer.scrollTop = messageContainer.scrollHeight;
}
function showLoading() {
const loadingId = 'loading-' + Date.now();
const loadingDiv = document.createElement('div');
loadingDiv.id = loadingId;
loadingDiv.className = 'flex mb-4';
loadingDiv.innerHTML = `
<div class="w-10 h-10 rounded-full bg-dev-accent bg-opacity-20 flex items-center justify-center mr-3">
<span class="text-lg">🤖</span>
</div>
<div class="message-bubble-ai rounded-lg p-3">
<div class="flex space-x-2">
<div class="w-2 h-2 bg-dev-accent rounded-full animate-bounce"></div>
<div class="w-2 h-2 bg-dev-accent rounded-full animate-bounce" style="animation-delay: 0.2s"></div>
<div class="w-2 h-2 bg-dev-accent rounded-full animate-bounce" style="animation-delay: 0.4s"></div>
</div>
</div>
`;
messageContainer.appendChild(loadingDiv);
messageContainer.scrollTop = messageContainer.scrollHeight;
return loadingId;
}
function removeLoading(loadingId) {
const loadingDiv = document.getElementById(loadingId);
if (loadingDiv) {
loadingDiv.remove();
}
}
function fileToBase64(file) {
return new Promise((resolve, reject) => {
const reader = new FileReader();
reader.readAsDataURL(file);
reader.onload = () => resolve(reader.result.split(',')[1]);
reader.onerror = error => reject(error);
});
}
function escapeHtml(unsafe) {
return unsafe
.replace(/&/g, "&")
.replace(/</g, "<")
.replace(/>/g, ">")
.replace(/"/g, """)
.replace(/'/g, "'");
}
// Image zoom functionality
window.openImageModal = function(imageSrc) {
const modal = document.getElementById('imageModal');
const modalImg = document.getElementById('modalImage');
modal.style.display = 'block';
modalImg.src = imageSrc;
}
// Close modal when clicking the X or outside the image
document.getElementById('imageModal').addEventListener('click', function(e) {
if (e.target === this || e.target.className === 'close') {
this.style.display = 'none';
}
});
// Theme toggle functionality
const themeToggle = document.getElementById('themeToggle');
const body = document.body;
themeToggle.addEventListener('click', function() {
const html = document.documentElement;
html.classList.toggle('dark');
if (html.classList.contains('dark')) {
themeToggle.innerHTML = '☀️ Light';
} else {
themeToggle.innerHTML = '🌙 Dark';
}
});
});
</script>
</body>
</html>
EOF
Running the AI Agent
Configure AWS credentials in the terminal. The assumed role or user should have access to Amazon Bedrock models.
export AWS_REGION=us-east-1
Start the Application
./mvnw spring-boot:run
Test the Application
- Open your browser and navigate to
http://localhost:8080
- Try some sample interactions:
Hi, my name is Alex. Who are you?
The AI agent will respond, but its responses will be generic.
Let's fix this and add an AI Agent Persona.
Stop the application with Ctrl+C.
Commit Changes
git add .
git commit -m "Create the AI agent"
Defining System Prompt and Persona
The system prompt defines the AI agent's personality and behavior:
ChatService.java
...
public static final String SYSTEM_PROMPT = """
You are a helpful and honest AI Agent for our company.
You can help with questions related to travel and expenses.
Follow these guidelines strictly:
1. ACCURACY FIRST: Only provide information you are confident about based on your training data.
2. ADMIT UNCERTAINTY: If you are unsure about any fact, detail, or answer, respond with "I don't know" or "I'm not certain about that."
3. NO SPECULATION: Do not guess, speculate, or make up information. It's better to say "I don't know" than to provide potentially incorrect information.
4. PARTIAL KNOWLEDGE: If you know some aspects of a topic but not others, clearly state what you know and what you don't know.
5. SOURCES: Do not claim to have access to real-time information, current events after your training cutoff, or specific databases unless explicitly provided.
6. TABLE FORMAT: Always use clean markdown tables for structured data presentation.
Example responses:
- "I don't know the current stock price of that company."
- "I'm not certain about the specific details of that recent event."
- "I don't have enough information to answer that question accurately."
Remember: Being honest about limitations builds trust. Always choose "I don't know" over potentially incorrect information.
""";
...
this.chatClient = chatClientBuilder
.defaultSystem(SYSTEM_PROMPT)
.build();
...
This gives the AI agent:
- A specific business context (Travel and Expenses Agent)
- Behavioral guidelines (friendly, helpful, concise)
- Honesty constraints (admit when it doesn't know something)
Test the Application
Now you can test the AI Agent using the Web UI:
Hi, my name is Alex. Who are you?
or the REST API directly:
curl -X POST http://localhost:8080/api/chat \
-H "Content-Type: application/json" \
-d '{"prompt": Hi, my name is Alex. Who are you?"}' \
--no-buffer
Nice! Now the AI Agent has a persona which is aligned with our requirements!
Let's continue the chat:
What is my name?
Unfortunately, the AI Agent doesn't remember my name, even though I provided it.
We will fix this in the next part of the blog series! Stay tuned!
Commit Changes
git add .
git commit -m "Add the Persona"
Cleaning Up
To stop the application, press Ctrl+C in the terminal where it's running.
Conclusion
In this blog post, we've successfully created a complete AI agent application using Java and Spring AI with Amazon Bedrock integration. We've covered:
- Setting up a Spring Boot project with Spring AI dependencies
- Configuring Amazon Bedrock integration
- Creating REST APIs with streaming responses
- Building a modern web interface with real-time chat
- Implementing AI agent personas with system prompts
The application demonstrates the power of Spring AI's unified programming model, making it easy to integrate sophisticated AI capabilities into Java applications. The streaming responses provide an excellent user experience, while the persona system allows for customized AI behavior.
In the next part of this series, we'll explore advanced features of the AI agent.
Let's continue building intelligent Java applications with Spring AI!
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