Deploying Agentic AI at Scale with Amazon Bedrock AgentCore

Introduction

Amazon Bedrock AgentCore provides a foundational, secure, and fully managed runtime for deploying agentic AI applications at enterprise scale. As organizations increasingly adopt generative AI for automation, decision‑making, and workflow orchestration, there is a growing need for structured agent frameworks that support tools, memory, identity management, and multi‑agent interactions. Amazon Bedrock AgentCore addresses these challenges by offering a ready‑to‑use operational backbone, allowing developers to focus on agent behavior while AWS handles concurrency, scaling, isolation, and observability. Combined with Strands Agents, a lightweight and flexible agent framework, developers can build reasoning pipelines, multi-tool agents, and even multi-agent systems that work seamlessly with Amazon Bedrock models, such as Claude, Amazon Nova, or Titan.

Pioneers in Cloud Consulting & Migration Services

Reduced infrastructural costs
Accelerated application deployment

Get Started

Key Features of Amazon Bedrock AgentCore

Amazon Bedrock AgentCore includes several capabilities that simplify agent development:

Managed Runtime: Automatically scales agent execution across sessions with no servers to manage.
Identity and Access Control: Provides secure, IAM-based access to AWS services, such as Amazon S3 and AWS Lambda.
Memory System: Supports short‑term memory per session and long‑term memory for user‑specific personalization.
Tool Gateway: Enables developers to expose APIs, AWS Lambda functions, or Python utilities as tools callable by the agent.
Observability: Tracks logs, traces, and metrics across agent executions and tool calls.
Versioning & Runtime Isolation: Each deployment becomes an immutable runtime version, ensuring session‑level isolation.

Benefits of Using Amazon Bedrock AgentCore

Production‑Ready Foundation: No need to implement session management, concurrency handling, or scaling logic.
Unified Tooling Layer: Consistent interface for calling AWS Lambda, REST APIs, or local Python functions.
Secure Execution: AWS IAM roles and fine-grained permissions ensure safe access to AWS resources.
Multi‑Framework Support: Compatible with Strands, LangChain, and custom Python frameworks.
Scalable to Enterprise Workloads: Ideal for customer support bots, automation agents, interview assessment systems, and analytics assistants.

Use Cases of Amazon Bedrock AgentCore

Conversational AI Assistants with tool‑based capabilities.
Document analysis bots using long‑context Amazon Bedrock models.
Developer automation agents for troubleshooting, deployment, or monitoring.
Enterprise decision-support systems integrating AWS services.
Multi‑agent systems use Strands A2A communication patterns.

Getting Started with Amazon Bedrock AgentCore

To begin using Amazon Bedrock AgentCore, developers typically install the Amazon Bedrock AgentCore toolkit, clone the official sample repository, and initialize a BedrockAgentCoreApp instance. The application defines an entry point that acts as the invocation handler. Tools are created using the @tool decorator in Strands, and models are loaded from Amazon Bedrock using the Amazon Bedrock Model class. The developer can then run the runtime locally and deploy using the AgentCore CLI.

Example 1: Basic Strands Agent with Amazon Bedrock AgentCore

Below is a fully functioning minimal example:

from strands import Agent, tool
from strands.models import BedrockModel
from bedrock_agentcore.runtime import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@tool
def greet(name: str):
    return f"Hello {name}, welcome to AgentCore!"

model = BedrockModel(model_id="anthropic.claude-3-7-sonnet-20250219-v1:0")

agent = Agent(
    model=model,
    tools=[greet],
    system_prompt="You are a helpful assistant who uses tools when needed."
)

@app.entrypoint
def handler(event):
    user_prompt = event.get('prompt')
    result = agent(user_prompt)
    return result.message['content'][0]['text']

from strands import Agent, tool
from strands.models import BedrockModel
from bedrock_agentcore.runtime import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@tool
def greet(name: str):
    return f"Hello {name}, welcome to AgentCore!"

model = BedrockModel(model_id="anthropic.claude-3-7-sonnet-20250219-v1:0")

agent = Agent(
    model=model,
    tools=[greet],
    system_prompt="You are a helpful assistant who uses tools when needed."
)

@app.entrypoint
def handler(event):
    user_prompt = event.get('prompt')
    result = agent(user_prompt)
    return result.message['content'][0]['text']

from strands import Agent, tool

from strands.models import BedrockModel

from bedrock_agentcore.runtime import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@tool

def greet(name: str):

return f"Hello {name}, welcome to AgentCore!"

model = BedrockModel(model_id="anthropic.claude-3-7-sonnet-20250219-v1:0")

agent = Agent(

model=model,

tools=[greet],

system_prompt="You are a helpful assistant who uses tools when needed."

)

@app.entrypoint

def handler(event):

user_prompt = event.get('prompt')

result = agent(user_prompt)

return result.message['content'][0]['text']

from strands import Agent, tool

from strands.models import BedrockModel

from bedrock_agentcore.runtime import BedrockAgentCoreApp

app = BedrockAgentCoreApp()

@tool

def greet(name: str):

return f"Hello {name}, welcome to AgentCore!"

model = BedrockModel(model_id="anthropic.claude-3-7-sonnet-20250219-v1:0")

agent = Agent(

model=model,

tools=[greet],

system_prompt="You are a helpful assistant who uses tools when needed."

)

@app.entrypoint

def handler(event):

user_prompt = event.get('prompt')

result = agent(user_prompt)

return result.message['content'][0]['text']

This example demonstrates how Strands handles agent reasoning while Amazon Bedrock AgentCore manages runtime execution, identity, and session lifecycle. Developers can test locally by running app.run() and sending a POST request to /invocations.

Example 2: Adding AWS Integration Tools

Tools can interact with AWS services through IAM-secured calls:

@tool
def fetch_s3_object(bucket: str, key: str):
    import boto3
    s3 = boto3.client('s3')
    obj = s3.get_object(Bucket=bucket, Key=key)
    return obj['Body'].read().decode()

@tool
def call_lambda(function_name: str, payload: dict):
    import boto3, json
    lam = boto3.client('lambda')
    response = lam.invoke(FunctionName=function_name, Payload=json.dumps(payload))
    return response['Payload'].read().decode()
@tool
def fetch_s3_object(bucket: str, key: str):
    import boto3
    s3 = boto3.client('s3')
    obj = s3.get_object(Bucket=bucket, Key=key)
    return obj['Body'].read().decode()

@tool
def call_lambda(function_name: str, payload: dict):
    import boto3, json
    lam = boto3.client('lambda')
    response = lam.invoke(FunctionName=function_name, Payload=json.dumps(payload))
    return response['Payload'].read().decode()

@tool

def fetch_s3_object(bucket: str, key: str):

import boto3

s3 = boto3.client('s3')

obj = s3.get_object(Bucket=bucket, Key=key)

return obj['Body'].read().decode()

@tool

def call_lambda(function_name: str, payload: dict):

import boto3, json

lam = boto3.client('lambda')

response = lam.invoke(FunctionName=function_name, Payload=json.dumps(payload))

return response['Payload'].read().decode()

@tool

def fetch_s3_object(bucket: str, key: str):

import boto3

s3 = boto3.client('s3')

obj = s3.get_object(Bucket=bucket, Key=key)

return obj['Body'].read().decode()

@tool

def call_lambda(function_name: str, payload: dict):

import boto3, json

lam = boto3.client('lambda')

response = lam.invoke(FunctionName=function_name, Payload=json.dumps(payload))

return response['Payload'].read().decode()

These tools allow the agent to retrieve documents from Amazon S3, invoke AWS Lambda functions, or integrate with enterprise APIs. Amazon Bedrock AgentCore’s identity system ensures that only authorized resources are accessed.

Execution Flow with Amazon API Gateway

A typical flow for invoking an agent through Amazon API Gateway looks like this:

Amazon API Gateway receives a user request and forwards it to Amazon Bedrock AgentCore’s /invocations endpoint.
AgentCore assigns or reuses a runtimeSessionId for the user.
Memory for the session is loaded (if applicable).
The Strands agent runs reasoning steps and tool calls.
Amazon Bedrock AgentCore captures all logs, traces, and request metadata.
A streaming or structured response is returned to the Amazon API Gateway integration.

Technical Challenges and Optimizations

Use Amazon S3 for large payloads instead of passing them directly into the agent.
Use async tasks for long-running operations to avoid blocking sessions.
Keep tool interfaces small and modular to reduce response latency.
Use Strands A2A communication for multi-agent orchestration.
Monitor tracing data to identify performance bottlenecks.

Conclusion

Amazon Bedrock AgentCore enables organizations to build reliable, scalable, and secure agentic AI systems without constructing the underlying infrastructure.

When combined with Strands, developers gain a powerful framework for reasoning, tool invocation, and multi-agent collaboration. Whether building automation workflows, enterprise AI assistants, or compliance-driven systems, this integration provides all the building blocks required to move from prototypes to production with confidence.

Drop a query if you have any questions regarding Amazon Bedrock AgentCore and we will get back to you quickly.

Empowering organizations to become ‘data driven’ enterprises with our Cloud experts.

Reduced infrastructure costs
Timely data-driven decisions

Get Started

About CloudThat

CloudThat is an award-winning company and the first in India to offer cloud training and consulting services worldwide. As a Microsoft Solutions Partner, AWS Advanced Tier Training Partner, and Google Cloud Platform Partner, CloudThat has empowered over 850,000 professionals through 600+ cloud certifications winning global recognition for its training excellence including 20 MCT Trainers in Microsoft’s Global Top 100 and an impressive 12 awards in the last 8 years. CloudThat specializes in Cloud Migration, Data Platforms, DevOps, IoT, and cutting-edge technologies like Gen AI & AI/ML. It has delivered over 500 consulting projects for 250+ organizations in 30+ countries as it continues to empower professionals and enterprises to thrive in the digital-first world.

FAQs

1. What models are supported in Amazon Bedrock AgentCore?

ANS: – Amazon Bedrock AgentCore works with all Amazon Bedrock foundation models, including Claude 3.7, Amazon Nova, and Titan models. You can reference models using their Amazon Bedrock model IDs in your Strands Agent configuration.

2. Can Amazon Bedrock AgentCore call AWS Lambda and external APIs?

ANS: – Yes. Using Strands tools, developers can expose AWS Lambda functions, REST endpoints, or internal utilities as callable tools. AgentCore securely handles AWS IAM permissions for these calls.

3. How does Amazon Bedrock AgentCore manage session context?

ANS: – Amazon Bedrock AgentCore uses runtimeSessionId to maintain short‑term session memory. Long‑term context can be stored using the Amazon Bedrock AgentCore memory subsystem for personalization or multi‑interaction workflows.

WRITTEN BY Ahmad Wani

Ahmad works as a Research Associate in the Data and AIoT Department at CloudThat. He specializes in Generative AI, Machine Learning, and Deep Learning, with hands-on experience in building intelligent solutions that leverage advanced AI technologies. Alongside his AI expertise, Ahmad also has a solid understanding of front-end development, working with technologies such as React.js, HTML, and CSS to create seamless and interactive user experiences. In his free time, Ahmad enjoys exploring emerging technologies, playing football, and continuously learning to expand his expertise.