Getting Started with Amazon Bedrock Agents
AI has revolutionized how we build smarter and more efficient systems, with large language models (LLMs) at the forefront of this transformation. Thanks to their advanced natural language processing capabilities, these models excel at understanding context and generating meaningful responses. However, they have inherent limitations—they cannot access real-time knowledge or interact with external systems. Amazon Bedrock Agents provide the perfect solution to these challenges and empower LLMs to take intelligent actions. By enhancing LLMs with features like action execution and external system interaction, Bedrock Agents unlock the potential for building truly dynamic and intelligent applications.
In this Cloud Lab, you’ll explore the power of Amazon Bedrock Agents and their ability to significantly enhance the capabilities of large language models (LLMs) by building and improving an application step by step. You’ll begin by creating essential resources like IAM roles and DynamoDB tables that’ll be used for access control and application storage. After that, you’ll develop an application integrated with an LLM provided by Bedrock to showcase the value of AI in applications. Next, you’ll replace this direct interaction with a Bedrock Agent, making the workflow more structured and efficient. Finally, you’ll introduce action groups by integrating a Lambda function, enabling the agent to perform a real-world task by interacting with an external system, a DynamoDB table. Through this progression, you’ll see how Bedrock Agents make AI-powered applications cleaner, more efficient, and more powerful.
By the end of this Cloud Lab, you’ll clearly understand how to integrate Bedrock’s LLMs into applications, streamline workflows with agents for improved efficiency, and expand your application’s capabilities using action groups. You’ll also gain hands-on experience structuring AI-driven workflows, enabling more intelligent and dynamic interactions within your applications.
Here’s a high-level architecture diagram of the infrastructure that you’ll create in this lab:
What makes an AI system “agentic”?
An agentic AI system doesn’t just respond to prompts; it decides what to do next. That typically involves interpreting intent, selecting tools, executing actions, and using the results to guide subsequent steps before producing a final response.
This shift matters because many real-world tasks are procedural:
Answering questions that require looking things up.
Performing actions in external systems.
Following rules and workflows.
Handling multi-step user requests.
Agentic systems are designed to handle that complexity in a structured way.
The core components of agent-based workflows
Most agent systems, regardless of tooling, share a few foundational elements:
Intent understanding: The agent determines what the user is requesting and what steps may be required.
Tool access: Agents use tools such as APIs, functions, and retrieval systems to fetch data or take actions, rather than relying on guesswork.
Planning and execution: The agent decides the order of steps, runs them, and adapts if intermediate results change.
State and memory: Agents often track intermediate context to keep multi-step tasks coherent.
Guardrails and constraints: Rules, schemas, and permissions limit what the agent can do and how it responds.
Where Amazon Bedrock Agents fit
Amazon Bedrock Agents offer a managed approach to building agentic workflows on AWS. Bedrock supplies access to foundation models, while agents add structure for tool use, orchestration, and execution.
The broader value isn’t a specific service, it’s the architecture pattern:
Clear separation between reasoning and actions.
Defined tool contracts instead of free-form calls.
Repeatable workflows that are easier to test and monitor.
Common use cases for AI agents
Agent-based systems are commonly used for:
Customer support and internal assistants.
Workflow automation and ticket handling.
Data retrieval and summarization.
Multi-step decision support.
Integrations across multiple services.
In all cases, the goal is the same: move from “chatbot” behavior to predictable, action-oriented systems.
Designing agents that behave reliably
Agent failures tend to fall into a few categories: unclear goals, excessive tool usage, weak constraints, or inadequate observability. Teams usually improve reliability by:
Keeping agent roles narrowly defined.
Using tools for facts and actions, not text generation.
Structuring inputs and outputs with schemas.
Logging decisions and intermediate steps.
Evaluating workflows with realistic test scenarios.
Agentic systems work best when treated as software systems, not just prompts.