Building Serverless Agentic Workflows with Amazon Bedrock

In the rapidly evolving world of AI-driven automation, serverless computing has become a game-changer, enabling developers to build scalable, efficient applications without worrying about infrastructure management. Amazon Bedrock, a fully managed service from AWS, allows us to combine the power of serverless computing with advanced AI models, enabling the creation of intelligent workflows powered by AI agents. In this blog, we’ll explore the Serverless Agentic Workflows with Amazon Bedrock course and provide some coding examples to help you get started.

What is Amazon Bedrock?

Amazon Bedrock is a fully managed service that provides developers access to foundation models (FMs) from multiple AI providers such as AI21 Labs, Anthropic, Stability AI, and Amazon Titan. It allows you to easily build and scale AI-driven applications using these models, all within a serverless environment, removing the need for provisioning and managing infrastructure.

With Serverless Agentic Workflows, you can automate complex tasks and workflows using AI agents, which can interact with external systems, make decisions, and even invoke APIs or retrieve data.

Key Concepts from the Course

The course "Serverless Agentic Workflows with Amazon Bedrock" focuses on building intelligent workflows using Amazon Bedrock. Here are the main lessons covered in the course:

Creating Your First Serverless Agent:

Learn to initialize an AI agent and integrate it with your workflows.

Connecting to External Services:

Make the agent interact with real-world systems, such as CRMs or databases.

Equipping the Agent with a Code Interpreter:

Enable the agent to perform complex calculations and make data-driven decisions.

Implementing Guardrails:

Add security and ethical guardrails to control the agent’s behavior.

Connecting to Support Documents:

Use Retrieval-Augmented Generation (RAG) to allow your agent to query documents and resolve issues autonomously.
Let’s break down these lessons with coding examples!

1. Creating Your First Serverless Agent

In this section, we’ll initialize an AI agent using Amazon Bedrock. The agent can be used to handle tasks like customer support, product recommendation, or simple Q&A.

Here’s how you can set up your first serverless agent;

Prerequisites

• AWS account
• Amazon Bedrock service access
• Basic knowledge of Python Let's initialize an AI Agent

import boto3

# Initialize the Amazon Bedrock client
client = boto3.client('bedrock')

# Set up the parameters for the agent
parameters = {
    'modelId': 'AmazonTitan',
    'input': 'Hello, how can I assist you today?'
}

# Call the model to initialize the agent
response = client.invoke_model(**parameters)

# Output the agent's response
print(response['output'])

This code initializes an Amazon Titan model using the invoke_model function and sends a simple query: "Hello, how can I assist you today?". The agent’s response will be printed, showing that it can generate dynamic answers.

2. Connecting to External Services

To make the AI agent more powerful, we can integrate it with external services like a CRM or ticketing system. For example, we can connect to a database to fetch customer records.

Fetching Data from a Database

import boto3

# Set up DynamoDB client
dynamodb = boto3.client('dynamodb')

def fetch_customer_info(customer_id):
    response = dynamodb.get_item(
        TableName='CustomerRecords',
        Key={'customer_id': {'S': customer_id}}
    )
    return response.get('Item', {})

# Fetch customer info
customer_id = '12345'
customer_info = fetch_customer_info(customer_id)

print(customer_info)

In this example, the code connects to a DynamoDB table called CustomerRecords and fetches the information for a specific customer. This integration makes the agent capable of retrieving and using real-time customer data to make informed decisions.

3. Equipping the Agent with a Code Interpreter

The agent can be equipped with the ability to perform code execution or calculations, helping it make data-driven decisions.

# Initialize the Bedrock client
client = boto3.client('bedrock')

# Code for calculating product prices
def calculate_price(base_price, discount):
    return base_price - (base_price * discount / 100)

# Implement logic with agent
def agent_with_code_interpreter():
    base_price = 100  # Sample base price
    discount = 10     # Sample discount percentage
    final_price = calculate_price(base_price, discount)
    return f"The final price after a {discount}% discount is: ${final_price}"

# Call the agent
response = client.invoke_model(
    modelId='AmazonTitan',
    input=agent_with_code_interpreter()
)

print(response['output'])

Here, the agent performs a simple price calculation, reducing a base price by a specified discount percentage. This demonstrates how agents can use custom code logic to enhance their decision-making ability.

4. Implementing Guardrails

Guardrails ensure that the AI agent behaves responsibly. For example, you can add logic to prevent the agent from revealing sensitive information or making unethical decisions.

def guardrails(agent_output):
    prohibited_keywords = ['password', 'credit card']
    for keyword in prohibited_keywords:
        if keyword in agent_output:
            return "Sorry, I cannot provide that information."
    return agent_output

# Example agent output
agent_output = "Here is your password: 12345"

# Apply guardrails
safe_output = guardrails(agent_output)
print(safe_output)

In this code, the agent’s output is filtered for sensitive information like passwords. If any such information is detected, the agent responds with a safe message.

5. Connecting to Support Documents with Retrieval-Augmented Generation (RAG)

Using RAG, your agent can query a database of support documents to retrieve the most relevant information when a customer asks a question.

# Initialize the Bedrock client
client = boto3.client('bedrock')

# Define a function to query support documents
def query_support_documents(query):
    response = client.retrieve_documents(
        query=query,
        document_store='SupportDocsStore'
    )
    return response['documents'][0]  # Return the first relevant document

# Query the support documents
query = "How do I reset my password?"
document = query_support_documents(query)

print(document)

In this code, the agent queries a document store (e.g., a collection of FAQ articles) to find the best document that answers the user's question about resetting a password.

Conclusion

In this blog, we’ve explored how to build Serverless Agentic Workflows with Amazon Bedrock. We covered the key course lessons and provided practical coding examples to illustrate how AI agents can interact with external services, perform calculations, and retrieve information. By combining serverless architecture with AI models, Amazon Bedrock makes it easier than ever to create scalable, intelligent applications that can handle everything from customer support to document processing.

If you're interested in diving deeper, the Serverless Agentic Workflows with Amazon Bedrock course offers a step-by-step guide to creating and managing these workflows. Whether you're looking to automate customer interactions, integrate with external services, or build ethical guardrails for your AI agents, this course has you covered.

References:

• Serverless Agentic Workflows with Amazon Bedrock Course: https://learn.deeplearning.ai/courses/serverless-agentic-workflows-with-amazon-bedrock/lesson/1/introduction