Build autonomous AI agents with Amazon Bedrock and Spring AI

Enterprise Java developers are increasingly tasked with bringing generative AI capabilities into legacy systems and modern cloud-native microservices. While Python traditionally dominates the machine learning landscape, the Spring ecosystem has matured rapidly through Spring AI, offering a first-class abstraction layer for Large Language Models. When combined with the enterprise-grade infrastructure of Amazon Bedrock, Java teams can build secure, scalable, and production-ready autonomous AI agents. To streamline development and reduce latency across multiple foundation models, many architectural teams integrate solutions like n1n.ai for unified API access and cost-effective model routing.

Architecture Overview of Java AI Agents

An autonomous AI agent differs from a standard conversational chatbot by possessing the ability to reason, plan, and execute actions dynamically. Instead of relying solely on pre-trained weights, an agent evaluates a user query, decides which tools or APIs to invoke, processes the output, and iterates until the goal is achieved.

In our Spring AI implementation, the architecture relies on several core components:

1. ChatClient & ChatModel: The primary entry points for interacting with foundation models hosted on Amazon Bedrock, such as Claude 3.5 Sonnet or DeepSeek-V3.
2. Tool Callbacks: Java methods annotated and exposed to the LLM, enabling the agent to fetch live data, query databases, or execute transactional logic.
3. ChatMemory: Persistent conversation history management to maintain context across multi-turn interactions.
4. Resilience4j Integration: Circuit breakers and rate limiters to handle intermittent API failures and latency spikes gracefully.

To manage the high throughput required by enterprise agents, developers can configure their Spring applications to route requests through n1n.ai, ensuring optimal load balancing and high availability across various LLM providers.

Setting Up Spring AI with Amazon Bedrock

To get started, configure your Maven or Gradle project with the necessary Spring Boot and Spring AI dependencies. Ensure you are using Spring Boot 3.3 or higher for optimal compatibility.

<dependencies>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-amazon-bedrock-spring-boot-starter</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
</dependencies>

Configure your application properties in application.yml to authenticate with AWS and select your default foundational model:

spring:
  ai:
    bedrock:
      aws:
        region: us-east-1
        access-key: ${AWS_ACCESS_KEY_ID}
        secret-key: ${AWS_SECRET_ACCESS_KEY}
      chat:
        enabled: true
        options:
          model: anthropic.claude-3-5-sonnet-20241022-v2:0
          temperature: 0.2
          max-tokens: 2048

Implementing Tool Calling and Function Execution

Tool calling allows the LLM to request data or trigger external systems by generating structured JSON payloads that map to Java methods. Spring AI simplifies this process through @Tool annotations and function callbacks.

Here is an example of a weather and inventory retrieval tool:

import org.springframework.ai.tool.annotation.Tool;
import org.springframework.stereotype.Component;

@Component
public class EnterpriseTools {

    @Tool(description = "Retrieve real-time inventory levels for a given SKU ID")
    public InventoryStatus checkInventory(String skuId) {
        // Business logic to query warehouse database
        return databaseService.queryStock(skuId);
    }
}

Register this tool with your ChatClient builder bean:

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class AgentConfiguration {

    @Bean
    public ChatClient agentChatClient(ChatClient.Builder builder, EnterpriseTools enterpriseTools) {
        return builder
            .defaultSystem("You are an autonomous enterprise operations assistant. Use available tools to answer user queries accurately.")
            .defaultTools(enterpriseTools)
            .build();
    }
}

Adding Conversation Memory and Resilience Patterns

Production agents must remember user context and handle upstream API timeouts without crashing. Spring AI provides robust abstractions for chat memory using relational databases or in-memory stores like Redis.

import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

@Configuration
public class MemoryConfig {

    @Bean
    public ChatMemory chatMemory() {
        return new InMemoryChatMemory();
    }
}

To protect your agent against downtime or rate limits from LLM providers, integrate Resilience4j:

import io.github.resilience4j.circuitbreaker.annotation.CircuitBreaker;
import org.springframework.stereotype.Service;

@Service
public class ResilientAgentService \{

    private final ChatClient chatClient;

    public ResilientAgentService(ChatClient chatClient) \{
        this.chatClient = chatClient;
    \}

    @CircuitBreaker(name = "bedrockService