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Building AI Agents with Amazon Bedrock: From LLMs to Autonomous Workflows

Explore how AI agents go beyond traditional chatbots by reasoning, using tools, retrieving information, and executing multi-step tasks. Learn how Amazon Bedrock can help developers build intelligent, scalable AI-powered applications.

Alok Kumar

AI/ML Developer

Building AI Agents with Amazon

Bedrock: From LLMs to Autonomous

Workflows

Introduction

Artificial Intelligence is evolving rapidly. Traditional applications follow predefined instructions, while modern AI applications can understand natural language, retrieve information, use external tools, and perform multi-step tasks.
One exciting development in this space is AI agents.
AI agents combine large language models (LLMs), tools, context, and decision-making workflows to solve problems that would otherwise require multiple manual steps.
For developers interested in building practical AI applications, AWS provides services that can help simplify this process. One of them is Amazon Bedrock, which offers access to foundation models and capabilities for developing generative AI applications.
In this article, we will explore how AI agents work, why they matter, and how AWS services can support their development.

1. What Is an AI Agent?

An AI agent is a software system that uses an AI model to determine what actions are needed to achieve a goal.
Unlike a basic chatbot that primarily generates text, an AI agent can interact with tools and external systems when appropriate.
For example, imagine an AI assistant that helps a developer investigate an application error.
Instead of simply explaining the error, the agent could:
  1. Understand the reported problem.
  1. Retrieve relevant documentation.
  1. Search application logs using an authorized tool.
  1. Analyze the collected information.
  1. Generate a summary with possible solutions.
The key difference is that an agent can coordinate multiple steps rather than responding with text alone.

2. Why Use Amazon Bedrock?

Amazon Bedrock is a managed AWS service that provides access to foundation models through a common API. It helps developers build generative AI applications without having to manage the underlying foundation-model infrastructure themselves.
Some useful capabilities include:
  • Foundation models: Choose from supported models for text generation, reasoning, and other generative AI tasks.
  • Knowledge Bases: Connect supported data sources to retrieve relevant information for grounded responses.
  • Guardrails: Configure safeguards to help filter undesirable content and sensitive information.
  • Agent capabilities: Build applications that coordinate model reasoning with defined actions and connected systems, depending on the selected Bedrock features.
  • AWS integration: Connect AI applications with other AWS services through appropriately configured permissions and integrations.
These capabilities can help developers move from a simple AI prototype toward a more complete application.

3. Understanding the Architecture

A practical AI agent can be organized into a few components:
User → Application → AI Model → Tools or Knowledge Sources → Response
Here is what each component does:
User interface: Accepts a question or task from the user.
Application layer: Validates requests, manages authentication, and coordinates the workflow.
Foundation model: Interprets the task and helps determine the next step.
Knowledge retrieval: Searches relevant documents or data when the application needs additional context.
Tools: Allow the application to perform explicitly permitted actions, such as retrieving records or checking service status.
Response layer: Combines the results and returns a useful answer to the user.
For example, a technical support agent could use a knowledge base to retrieve troubleshooting instructions and an authorized API to check a service's status before generating its final response.
The model does not automatically have access to every system. Developers must explicitly configure tools, permissions, and data access.

4. Example: Building a Documentation Assistant

Consider a company that maintains hundreds of technical documents. Developers frequently spend time searching for configuration instructions, troubleshooting guides, and API references.
A documentation assistant can make this process easier.
A possible workflow is:
  1. A developer asks, "Why is my application failing to connect to the database?"
  1. The application searches relevant documentation using a retrieval mechanism.
  1. The model analyzes the retrieved information alongside the user's question.
  1. If necessary, an authorized diagnostic tool retrieves additional information.
  1. The application produces a response with relevant explanations and suggested troubleshooting steps.
Amazon Bedrock Knowledge Bases can support retrieval-augmented generation (RAG), helping applications retrieve relevant information from connected knowledge sources.
RAG is particularly useful when answers need to reflect an organization's documentation rather than relying only on information learned during model training.

5. RAG and AI Agents: What Is the Difference?

RAG and AI agents are related, but they solve different problems.
Retrieval-Augmented Generation (RAG) retrieves relevant information and provides it to a model as context for generating an answer.
An AI agent can coordinate multiple actions to accomplish a goal. Those actions may include retrieving information, calling APIs, evaluating results, and deciding what to do next.
For example:
  • A RAG application answers questions using product documentation.
  • An AI agent could retrieve the documentation, check an authorized service endpoint, compare the results, and recommend the next troubleshooting step.
RAG can be a component of an AI agent. However, not every RAG application needs to be an agent, and not every agent requires RAG.
Choosing the simpler architecture when possible can reduce complexity, cost, and latency.

6. A Simple Python Starting Point

Developers can use the AWS SDK for Python, known as Boto3, to interact with supported Amazon Bedrock Runtime APIs.
First, install the SDK:
Plain text
pip install boto3
Configure your AWS credentials using an appropriate AWS authentication method, and make sure your account has access to the selected model in the target AWS Region.
A simplified example of sending a prompt to a model through the Converse API is shown below:
Plain text
import boto3

client = boto3.client(
    "bedrock-runtime",
    region_name="us-east-1"
)

response = client.converse(
    modelId="YOUR_SUPPORTED_MODEL_ID",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "text": (
                        "Explain how AI agents differ "
                        "from traditional chatbots."
                    )
                }
            ]
        }
    ],
    inferenceConfig={
        "maxTokens": 300,
        "temperature": 0.3
    }
)

answer = response["output"]["message"]["content"][0]["text"]
print(answer)
Replace  YOUR_SUPPORTED_MODEL_ID  with a model ID available to your account and Region.
This example demonstrates model inference, not a complete autonomous agent. To create an agentic workflow, you would add tools, application logic, state management, and appropriate permission checks.

7. Important Security Considerations

Building an AI agent involves more than selecting a capable model.
Agents may interact with APIs, databases, and business systems, so their actions must be carefully controlled.
Important practices include:
  • Follow the principle of least privilege when configuring AWS IAM permissions.
  • Validate user inputs and tool arguments.
  • Keep credentials and API keys out of source code.
  • Require human approval for sensitive or irreversible actions.
  • Log relevant operations while avoiding unnecessary exposure of personal or confidential information.
  • Test how the application handles incorrect information, malicious prompts, and tool failures.
  • Apply appropriate limits to tool usage, execution time, and model costs.
For production applications, developers should also evaluate response quality, latency, reliability, and operational costs.
An agent should not receive broad permissions simply because it uses an LLM.

8. What Comes Next?

Once the basic workflow is working, developers can
# ai# agentic-ai
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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