How I Built a Fully Functional AI Agent Chatbot in Just 2 Days — End-to-End with AWS Bedrock

From concept to deployment — powered by AWS Bedrock, Lambda, and API Gateway.

In the fast-paced world of AI development, being able to move from idea to deployment quickly is critical — especially when showcasing what you’re capable of. That’s exactly what I set out to do: design, build, and deploy an intelligent AI chatbot agent in just 2 days, entirely end-to-end.

In this post, I’ll walk you through the journey — from setting up the knowledge base to integrating with Lambda and creating a working frontend demo. Whether you're a developer or a business owner, you'll see how fast and powerful custom AI agents can be.

🧠 Step 1: Setting Up the Knowledge Base with AWS Bedrock

The foundation of any smart AI agent is a strong knowledge base. I used Amazon S3 as the data source and uploaded a simple .txt file containing key business FAQs and details.

Once the file was uploaded, I synced it within the Bedrock console to ensure it's indexed and available for real-time use. Bedrock allows you to choose custom embedding models and retrieval methods — offering powerful flexibility.

🤖 Step 2: Creating the Agent with Bedrock

Creating the agent itself is straightforward in AWS Bedrock:

I started by creating a basic agent and saving it.
Then, I jumped into the Agent Builder for custom configuration.

Here’s the custom prompt I used to guide the chatbot’s behavior:

Prompt Example:
“You are a sales chatbot for a company that builds custom chatbots for websites to automate customer support and lead generation...”

I also enabled memory features so the chatbot can retain past interactions — crucial for a natural, flowing conversation.

📚 Step 3: Linking the Knowledge Base

With the prompt and settings in place, I attached the previously created knowledge base to the agent. This enabled the bot to respond to user queries with rich, relevant information pulled from the data source.

The result? A context-aware, friendly, and professional chatbot — ready to assist users.

🛠 Step 4: Connecting with Lambda + API Gateway

Now comes the integration part. I created an AWS Lambda function using Python 3.12. The function interacts with the Bedrock agent runtime, takes user input, and returns the chatbot’s response.

Here’s a look at the Lambda logic:

import boto3
import json
import os

bedrock_client = boto3.client("bedrock-agent-runtime", region_name="us-east-1")
AGENT_ID = "FINC1****"
AGENT_ALIAS_ID = "XFZSG***"

def lambda_handler(event, context):
    try:
        body = json.loads(event.get("body", "{}"))
        user_input = body.get("inputText", "hi")
        session_id = body.get("sessionId", "default-session-001")

        response = bedrock_client.invoke_agent(
            agentId=AGENT_ID,
            agentAliasId=AGENT_ALIAS_ID,
            sessionId=session_id,
            inputText=user_input,
            enableTrace=True,
        )

        for event in response['completion']:
            if 'chunk' in event:
                data = event['chunk']['bytes'].decode('utf-8')
                response = data

        return {
            "statusCode": 200,
            "headers": { "Content-Type": "application/json" },
            "body": json.dumps({ "response": response })
        }

    except Exception as e:
        return {
            "statusCode": 500,
            "body": json.dumps({ "error": str(e) })
        }

📌 Pro Tip:

Increase Lambda timeout to 2 minutes
Create a dedicated IAM role with access to S3, Lambda, and Bedrock

🌐 Step 5: Exposing via API Gateway

To allow the chatbot to communicate with external apps or frontend interfaces, I connected the Lambda function to API Gateway (HTTP API).

Created a new API
Linked the Lambda function
Deployed the endpoint
Tested the setup using Postman

💻 Final Result: Live Chatbot Demo for a Business Use Case

Here’s a snapshot of the live chatbot built to assist a fictional chatbot-building company. It handles everything from customer queries to tier-based service explanations.

🔥 Why This Matters

This project wasn’t just about building a chatbot — it was a real-world demonstration of my ability to:

✅ Rapidly prototype intelligent systems
✅ Integrate multiple AWS services (Bedrock, Lambda, S3, API Gateway)
✅ Build production-ready AI agents for business use cases
✅ Deliver results fast — just 2 days from scratch to live demo

If you’re a business looking to automate conversations, generate leads, or scale your customer support — this is the kind of intelligent automation I can build for you.

👋 Let’s Connect

Whether you're a startup, agency, or enterprise, I specialize in building custom AI chatbot agents, LLM-powered workflows, and automation solutions tailored to your needs — fast.

📬 Interested in working together or want a demo?
Feel free to reach out: