Building AI Agents on AWS with Amazon Bedrock AgentCore
AI agents can go beyond generating text by using tools, accessing data, and taking actions. This article introduces agentic AI on AWS and explains how Amazon Bedrock AgentCore can help build and deploy production-ready agents.
Generative AI applications are moving from simple question-and-answer systems toward AI agents that can understand a goal, decide what actions are required, use tools, and complete multi-step tasks.
For example, instead of simply answering:
"What is the status of my order?"
an AI agent could retrieve the order from a database, check its current status, and provide the result to the user.
What Makes an AI Agent Different?
A traditional generative AI application often follows:
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User
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Prompt
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AI Model
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ResponseAn agentic application adds tools and actions:
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User
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AI Agent
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+--------+---------+
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v v v
Database API Tool
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+--------+---------+
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ResponseThe agent can determine which tools are needed to accomplish the user's goal.
Agentic AI on AWS
AWS provides several capabilities for building agentic applications. Amazon Bedrock provides access to foundation models, while Amazon Bedrock AgentCore provides infrastructure and services for building, deploying, connecting, and operating AI agents.
AgentCore is designed to work with different agent frameworks and foundation models, giving developers flexibility when building their applications.
Some of its capabilities include:
- Runtime for securely running agents.
- Memory for maintaining useful context.
- Gateway for connecting agents with tools and APIs.
- Identity for managing agent access.
- Observability for monitoring agent behavior.
- Browser and Code Interpreter tools for additional agent capabilities.
Example: Customer Support Agent
Consider a customer support application.
A user asks:
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Can you check my order and tell me when it will arrive?The agent could:
- Understand the request.
- Identify the required customer information.
- Call an order-management API.
- Retrieve the delivery information.
- Generate a response.
The architecture could look like:
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Customer
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Web Application
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AI Agent
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+------> Order API
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+------> Customer Database
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+------> Knowledge Base
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Final ResponseThis is where agentic AI becomes more powerful than a chatbot that only generates text.
Why AWS Is Useful for Agents
Building an AI agent involves more than choosing a model. Production systems also need authentication, tool access, deployment, monitoring, memory, and security.
AgentCore is designed to provide these capabilities as managed services so developers can focus more on agent logic rather than building the surrounding infrastructure themselves.
AgentCore also supports popular agent frameworks and models, which can be useful when moving an existing prototype into a production environment.
Final Thoughts
AI agents represent an important shift in how we build AI applications.
Instead of simply asking a model to generate an answer, we can build systems that understand goals, retrieve information, use tools, and perform actions.
AWS provides the infrastructure needed to take these systems from prototypes toward production. With Amazon Bedrock and AgentCore, developers can combine foundation models with tools, data, memory, security, and observability to create more capable AI applications.
The next step for anyone learning agentic AI is to build a small agent that can use a real tool, such as an API, database, or knowledge base. That is where the difference between a chatbot and an AI agent becomes clear.
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