DEV Community

Cover image for AI Agent with Amazon Bedrock: A Brief Guide
Abdullateef OGUNDIPE
Abdullateef OGUNDIPE

Posted on

AI Agent with Amazon Bedrock: A Brief Guide

#aws #ai #lex

A Guide on AI Agent with Amazon Bedrock

From confused chatbots to intelligent AI agents — a step-by-step journey using Amazon Bedrock.

Introduction: When Chatbots Get It Wrong

Imagine asking a customer support chatbot a simple question — "I want to return my order" — and receiving a completely irrelevant response. Frustrating, right?

Despite the explosion of AI tools, many teams still struggle to build useful, context-aware AI assistants. The problem isn’t the lack of models — it’s the complexity of integration.

This learning post shows a brief guide on how to build powerful AI agent using

Amazon Bedrock, focusing on concepts, architecture, and a practical implementation path.

What You’ll Learn

By the end of this post, you will:

  • Understand what Amazon Bedrock is and why it’s different
  • Learn what makes an AI agent (beyond just text generation)
  • Design a real-world AI agent architecture
  • Build a simple AI assistant using AWS services
  • Discover practical use cases and next steps

What Is Amazon Bedrock?

Amazon Bedrock

Amazon Bedrock is a fully managed, serverless service that allows you to build generative AI applications using foundation models (FMs) from leading providers such as:

  • Anthropic (Claude)
  • Meta (Llama)
  • AI21 Labs
  • Amazon Titan

Why Bedrock Matters

  • 🚫 No infrastructure management
  • 🚫 No model training required
  • ✅ Enterprise-grade security
  • ✅ Easy integration with AWS services

You focus on logic and experience — AWS handles the heavy lifting.

Why AI Agents Matter Today

AI Agents

AI agents are more than chatbots. They:

  • Understand natural language
  • Maintain context
  • Take actions (via APIs or functions)
  • Reduce manual workflows

Real-World Agent Applications

  • Customer support assistants
  • HR onboarding bots
  • Internal knowledge search
  • Healthcare triage systems
  • Legal document summarization

Generative AI vs AI Agents

Generative AI AI Agent
Generates text Understands intent
Stateless Context-aware
No actions Calls APIs / functions
Output-only Decision + execution

An AI agent combines reasoning, memory, and action.

Manual vs AI Agent: A Return Process Example

🧍 Manual Return (Physical Store)

  • Walk to the store
  • Explain the issue
  • Clerk checks receipt
  • Forms are filled
  • Long waiting time

🤖 AI Agent Return (Digital Assistant)

"I want to return order 12345 because it’s broken"

  • AI understands intent instantly
  • Extracts order ID and reason
  • Calls backend return API
  • Confirms return — instantly

This is the power of AI agents.

Core Concepts Behind AI Agents

To build an AI agent, you need more than a model:

  • Natural Language Processing (NLP)
  • Prompt Engineering
  • Context & Memory Management
  • Knowledge Bases & Embeddings
  • Retrieval-Augmented Generation (RAG)

Amazon Bedrock supports these building blocks seamlessly.

What We’ll Build

We’ll create a simple AI agent that:

  • Uses Anthropic Claude via Amazon Bedrock
  • Accepts user input from a web UI
  • Processes requests with AWS Lambda
  • Exposes an API using API Gateway
  • Returns intelligent, contextual responses

Architecture Overview

Architecture Overview

Frontend

  • HTML / CSS / JavaScript (or chat widget)

⬇️

API Gateway

  • Secure REST endpoint

⬇️

AWS Lambda

  • Handles logic
  • Calls Amazon Bedrock

⬇️

Amazon Bedrock

  • Foundation model inference

Step-by-Step Build Summary

Step-by-Step

1️⃣ Choose a Foundation Model

  • Select Claude or Titan from Amazon Bedrock

2️⃣ Add a Knowledge Base (Optional)

  • Store documents in Amazon S3
  • Enable semantic search via embeddings

3️⃣ Create a Lambda Function

  • Use boto3 to invoke Bedrock
  • Handle prompt and response formatting

4️⃣ Build the Frontend

  • Simple chat interface
  • Send user input to API Gateway

Video link for step by step implementation Step by Step implemetation

Presentation: 10:00 - 42:00
Implementation : 43:00 -

Tools & Services Used

  • Amazon Bedrock – foundation models
  • AWS Lambda – backend logic
  • Amazon API Gateway – REST API
  • AWS IAM – secure permissions
  • HTML/CSS/JavaScript – frontend UI

Real-World Applications

  • 🏛️ Citizen service chatbots
  • 🏥 Healthcare assistants
  • ⚖️ Legal research tools
  • 🧑‍💼 HR knowledge bots

AI agents are becoming core digital employees.

Key Learning Resources

  • Amazon Bedrock Documentation
  • AWS IAM Best Practices
  • Lambda + API Gateway Integration

https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.htm
https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html
https://docs.aws.amazon.com/lambda/latest/dg/services-apigateway.html
https://www.youtube.com/watch?v=JBXSwvdJJ6Q

Start simple. Experiment fast. Let users feel the magic.

  • Amazon Bedrock YouTube Demos

Final Thoughts

AI agents are no longer "future tech" — they’re today’s advantage.

Amazon Bedrock removes the hardest barriers to entry, allowing developers to focus on use cases, logic, and user experience.

Start simple. Experiment fast. Let users feel the magic.

👋 Thanks for reading!

If you found this helpful, consider:

  • Leaving a reaction
  • Sharing with your team
  • Following for more AWS & AI learning posts

Happy building 🚀

Top comments (0)