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 autonomously interact with external systems. Amazon Bedrock AgentCore provides the perfect solution to these challenges. By wrapping LLMs in a managed orchestration environment (the Harness) and providing secure tool access (the Gateway), AgentCore empowers foundation models to take intelligent actions, unlocking the potential for truly dynamic and autonomous applications.

In this Cloud Lab, you’ll explore the power of Amazon Bedrock AgentCore and its ability to significantly enhance large language models by building and improving an application step by step. You’ll begin by creating essential resources like IAM roles and DynamoDB tables that will be used for access control and application storage. After that, you’ll develop an application integrated with a foundation model provided by Bedrock to showcase the baseline value of generative AI.

Next, you’ll replace this direct interaction with an AgentCore Harness, centralizing the AI logic and making the workflow more structured and efficient. Finally, you’ll introduce an AgentCore Gateway by integrating an AWS Lambda function as an MCP-compatible tool. This enables the agent to perform a real-world task by interacting with an external system and autonomously updating a DynamoDB table. Through this progression, you’ll see how AgentCore makes AI-powered applications cleaner, more efficient, and incredibly powerful.

By the end of this Cloud Lab, you’ll clearly understand how to integrate Bedrock’s LLMs into applications, orchestrate complex reasoning loops using the AgentCore Harness, and securely expand your application’s capabilities using AgentCore Gateways. You’ll also gain hands-on experience structuring AI-driven workflows, enabling intelligent, real-world 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 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 AgentCore fits

Amazon Bedrock AgentCore offers a powerful, decoupled architecture for building agentic workflows on AWS. While Bedrock supplies access to foundation models, AgentCore provides the orchestration layer. It introduces two primary components:

• Harness: The active execution environment that wraps the foundation model. It manages the agent's reasoning loop, state, and memory.

• Gateways: The central routing hubs that securely connect the Harness to external tools, data sources, and safety guardrails.

The broader value isn’t just a specific service; it’s the architecture pattern:

• Clear separation between reasoning (Harness) and actions (Gateways).

• Defined tool contracts (using standards like the Model Context Protocol) instead of free-form, unreliable function 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 via strict system prompts.

• Using Gateway tools for fetching facts and executing actions, not for generating conversational text.

• Structuring tool inputs and outputs with strict JSON schemas.

• Logging decisions and intermediate steps using inline agent traces.

• Evaluating workflows with realistic test scenarios.

Agentic systems work best when treated as structured software systems, not just complex prompts.