Amazon Bedrock AgentCore: Comprehensive Technical Guide

Framework Name: bedrock_agentcore

A complete technical reference for Amazon Bedrock AgentCore, the secure serverless runtime for building, deploying, and managing AI agents at enterprise scale. AgentCore provides a unified platform for agent development with support for multiple open-source frameworks, secure sandbox execution, and comprehensive AWS integration.

Announced: AWS Summit NYC 2025 Status: Generally Available Free Trial: Available until September 16, 2025 Last Updated: April 2026

Introduction to AgentCore
Core Features
AgentCore Architecture
Getting Started
Secure Sandbox Runtime
Supported Frameworks
Data Analysis and Workflow Automation
AgentCore APIs
Integration Patterns
Security and Compliance
Best Practices
Production Deployment

Introduction to AgentCore

Amazon Bedrock AgentCore is a secure serverless runtime that simplifies building and deploying AI agents with complex data analysis and workflow automation capabilities. It provides a managed environment for executing agent code with built-in security, scalability, and integration with AWS services.

Key Benefits

Benefit	Description	Value Proposition
Secure Sandbox	Isolated JavaScript, TypeScript, and Python execution	Enterprise-grade security
Framework Agnostic	Supports Strands, LangChain, LangGraph, CrewAI	Use your preferred framework
Serverless Runtime	No infrastructure management required	Reduced operational overhead
AWS Integration	Native integration with AWS services	Seamless enterprise workflows
Free Trial	Free until September 16, 2025	Risk-free evaluation

Use Cases

Complex Data Analysis: Run analytical queries on large datasets with code interpretation
Workflow Automation: Orchestrate multi-step business processes
Custom Agent Logic: Execute complex agent reasoning with full programming language support
Open-Source Framework Integration: Bring existing agent implementations to AWS

Core Features

1. Secure Serverless Runtime

AgentCore provides a secure, isolated execution environment for agent code:

from bedrock_agentcore import AgentCoreRuntime

# Create a secure runtime instance
runtime = AgentCoreRuntime(
    runtime_type='python3.11',
    timeout_seconds=300,
    memory_mb=1024,
    security_profile='enterprise'
)

# Execute agent code in secure sandbox
result = runtime.execute(
    code="""
import pandas as pd
import numpy as np

# Complex data analysis
data = pd.read_csv('sales_data.csv')
monthly_revenue = data.groupby('month')['revenue'].sum()
forecast = np.polyfit(range(len(monthly_revenue)), monthly_revenue, deg=2)
return {'monthly_revenue': monthly_revenue.to_dict(), 'forecast_coefficients': forecast.tolist()}
    """,
    context={
        'data_sources': ['s3://my-bucket/sales_data.csv'],
        'permissions': ['s3:GetObject']
    }
)

print(f"Analysis complete: {result['output']}")

2. Framework Support

AgentCore supports multiple open-source frameworks:

Framework	Language	Features	Use Case
Strands	Python	Lightweight, AWS-native	Quick prototyping, simple workflows
LangChain	Python	Rich ecosystem, RAG	Document analysis, knowledge bases
LangGraph	Python	State machines, graphs	Complex multi-step workflows
CrewAI	Python	Multi-agent collaboration	Team-based agent systems

3. Data Analysis Capabilities

Built-in support for complex data analysis:

from bedrock_agentcore import DataAnalysisAgent

# Create data analysis agent
analysis_agent = DataAnalysisAgent(
    model_id='anthropic.claude-3-5-sonnet-20241022-v2:0',
    enable_code_interpreter=True,
    allowed_libraries=['pandas', 'numpy', 'matplotlib', 'scikit-learn']
)

# Analyze sales data
result = analysis_agent.analyze(
    query="Analyze Q4 2024 sales trends and predict Q1 2025 revenue",
    data_sources=[
        's3://data-lake/sales/2024-q4.parquet',
        's3://data-lake/sales/historical.parquet'
    ],
    output_format='json'
)

print(result['insights'])
print(result['visualizations'])  # URLs to generated charts
print(result['predictions'])

4. Workflow Automation

Orchestrate complex business workflows:

from bedrock_agentcore import WorkflowOrchestrator

# Define workflow
workflow = WorkflowOrchestrator(
    name='InvoiceProcessing',
    description='Automated invoice processing and approval'
)

# Add workflow steps
workflow.add_step(
    name='extract_data',
    agent_type='document_extraction',
    input_source='s3://invoices/incoming/',
    output_target='dynamodb://processed-invoices'
)

workflow.add_step(
    name='validate_data',
    agent_type='data_validation',
    depends_on='extract_data',
    rules=['amount > 0', 'vendor_id exists', 'gl_code valid']
)

workflow.add_step(
    name='approval_routing',
    agent_type='decision_router',
    depends_on='validate_data',
    routing_logic={
        'amount < 1000': 'auto_approve',
        'amount >= 1000 and amount < 10000': 'manager_approval',
        'amount >= 10000': 'executive_approval'
    }
)

# Execute workflow
execution_id = workflow.start()
print(f"Workflow started: {execution_id}")

AgentCore Architecture

System Architecture

┌────────────────────────────────────────────────────────────────┐
│                        User Applications                        │
│                    (Console, SDK, API, CLI)                    │
└──────────────────────────┬─────────────────────────────────────┘
                           │
┌──────────────────────────▼─────────────────────────────────────┐
│                    AgentCore Control Plane                      │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐         │
│  │   Runtime    │  │   Framework  │  │   Security   │         │
│  │   Manager    │  │   Adapter    │  │   Manager    │         │
│  └──────────────┘  └──────────────┘  └──────────────┘         │
└──────────────────────────┬─────────────────────────────────────┘
                           │
┌──────────────────────────▼─────────────────────────────────────┐
│                  AgentCore Data Plane                           │
│  ┌──────────────────────────────────────────────────┐          │
│  │         Secure Sandbox Runtime Environment        │          │
│  │  ┌─────────┐  ┌─────────┐  ┌─────────┐          │          │
│  │  │ Python  │  │   JS    │  │   TS    │          │          │
│  │  │ Runtime │  │ Runtime │  │ Runtime │          │          │
│  │  └─────────┘  └─────────┘  └─────────┘          │          │
│  │                                                   │          │
│  │  Security Controls:                              │          │
│  │  - Network isolation                             │          │
│  │  - Resource limits                               │          │
│  │  - IAM integration                               │          │
│  │  - Audit logging                                 │          │
│  └──────────────────────────────────────────────────┘          │
└──────────────────────────┬─────────────────────────────────────┘
                           │
┌──────────────────────────▼─────────────────────────────────────┐
│                      AWS Service Integration                    │
│  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐  ┌────────┐  │
│  │   S3   │  │ Lambda │  │DynamoDB│  │Secrets │  │ Step   │  │
│  │        │  │        │  │        │  │Manager │  │Function│  │
│  └────────┘  └────────┘  └────────┘  └────────┘  └────────┘  │
└────────────────────────────────────────────────────────────────┘

Component Overview

Control Plane Components:

Runtime Manager: Manages runtime lifecycle, scaling, and resource allocation
Framework Adapter: Provides compatibility layer for different frameworks
Security Manager: Enforces security policies and IAM integration

Data Plane Components:

Secure Sandbox: Isolated execution environment with resource limits
Multi-Language Support: Python, JavaScript, TypeScript runtimes
Security Controls: Network isolation, IAM, audit logging

Getting Started

Prerequisites

AWS Account with Bedrock access
Python 3.8+ or Node.js 16+
AWS CLI configured
IAM role with AgentCore permissions

Installation

# Install AgentCore SDK for Python
pip install boto3 bedrock-agentcore

# Or for Node.js
npm install @aws-sdk/client-bedrock-agentcore

Quick Start Example

import boto3
from bedrock_agentcore import AgentCore

# Initialize AgentCore client
agentcore = AgentCore(region_name='us-east-1')

# Create your first AgentCore agent
agent = agentcore.create_agent(
    name='MyFirstAgentCoreAgent',
    description='A simple data analysis agent',
    runtime='python3.11',
    framework='strands',
    handler='main.handler',
    code_source='s3://my-bucket/agent-code.zip',
    role_arn='arn:aws:iam::ACCOUNT:role/AgentCoreRole'
)

print(f"Agent created: {agent['agentId']}")

# Invoke the agent
response = agentcore.invoke_agent(
    agent_id=agent['agentId'],
    input={
        'query': 'Analyze monthly sales trends for Q4 2024',
        'data_source': 's3://my-data/sales.csv'
    }
)

print(f"Analysis result: {response['output']}")

Secure Sandbox Runtime

Runtime Isolation

AgentCore provides strong isolation between agent executions:

from bedrock_agentcore import SandboxConfiguration

# Configure sandbox security
sandbox_config = SandboxConfiguration(
    # Network isolation
    network_mode='isolated',  # No internet access
    allow_aws_services=['s3', 'dynamodb', 'secretsmanager'],

    # Resource limits
    max_memory_mb=2048,
    max_cpu_cores=2,
    max_execution_time_seconds=300,
    max_disk_mb=1024,

    # Security policies
    enable_code_signing=True,
    require_encrypted_storage=True,
    audit_all_operations=True,

    # IAM integration
    execution_role='arn:aws:iam::ACCOUNT:role/AgentExecutionRole',
    assume_role_policy={
        'Effect': 'Allow',
        'Principal': {'Service': 'bedrock-agentcore.amazonaws.com'},
        'Action': 'sts:AssumeRole'
    }
)

# Create agent with sandbox configuration
agent = agentcore.create_agent(
    name='SecureAnalysisAgent',
    sandbox_config=sandbox_config,
    code_source='s3://secure-bucket/agent.zip'
)

Language Runtime Support

Python Runtime:

# Python 3.11 runtime with standard libraries
runtime_config = {
    'runtime': 'python3.11',
    'allowed_libraries': [
        'pandas', 'numpy', 'scikit-learn',
        'boto3', 'requests', 'matplotlib'
    ],
    'custom_layers': [
        'arn:aws:lambda:us-east-1:ACCOUNT:layer:CustomML:1'
    ]
}

JavaScript/TypeScript Runtime:

// Node.js 18 runtime
const runtimeConfig = {
    runtime: 'nodejs18.x',
    allowedModules: [
        '@aws-sdk/client-s3',
        '@aws-sdk/client-dynamodb',
        'axios',
        'lodash'
    ],
    tsconfig: {
        compilerOptions: {
            target: 'ES2020',
            module: 'commonjs',
            strict: true
        }
    }
};

Resource Management

from bedrock_agentcore import ResourceMonitor

# Monitor agent resource usage
monitor = ResourceMonitor(agent_id='agent-123')

# Get real-time metrics
metrics = monitor.get_current_metrics()
print(f"CPU Usage: {metrics['cpu_percent']}%")
print(f"Memory Usage: {metrics['memory_mb']} MB")
print(f"Execution Time: {metrics['execution_time_ms']} ms")

# Set up alerts
monitor.create_alert(
    metric='memory_mb',
    threshold=1500,
    action='throttle'  # or 'terminate', 'notify'
)

Supported Frameworks

1. Strands Agents SDK

Lightweight, AWS-native framework (covered in detail in separate guide)

from strands import Agent, tool
from bedrock_agentcore import integrate_strands

# Define agent with Strands
@tool
def analyze_data(data_path: str) -> dict:
    """Analyze sales data from S3"""
    import pandas as pd
    df = pd.read_csv(data_path)
    return {
        'total_revenue': df['revenue'].sum(),
        'avg_transaction': df['revenue'].mean()
    }

agent = Agent(
    name='SalesAnalyst',
    tools=[analyze_data],
    model='anthropic.claude-3-5-sonnet-20241022-v2:0'
)

# Deploy to AgentCore
agentcore_agent = integrate_strands(agent)

2. LangChain Integration

Rich ecosystem for RAG and document processing

from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain.tools import Tool
from langchain_community.chat_models import BedrockChat
from bedrock_agentcore import LangChainAdapter

# Create LangChain agent
llm = BedrockChat(model_id='anthropic.claude-3-5-sonnet-20241022-v2:0')

tools = [
    Tool(
        name='DocumentSearch',
        func=lambda q: vector_search(q),
        description='Search company documentation'
    )
]

agent = create_openai_functions_agent(llm, tools)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# Deploy to AgentCore
adapter = LangChainAdapter()
agentcore_agent = adapter.deploy(
    agent_executor=agent_executor,
    runtime='python3.11',
    name='DocumentAssistant'
)

3. LangGraph Integration

State machine-based workflows

from langgraph.graph import StateGraph, END
from bedrock_agentcore import LangGraphAdapter

# Define state machine
workflow = StateGraph()

# Add nodes
workflow.add_node('analyze', analyze_step)
workflow.add_node('validate', validate_step)
workflow.add_node('report', report_step)

# Add edges
workflow.add_edge('analyze', 'validate')
workflow.add_conditional_edges(
    'validate',
    lambda x: 'report' if x['valid'] else 'analyze'
)
workflow.add_edge('report', END)

# Set entry point
workflow.set_entry_point('analyze')

# Compile and deploy
app = workflow.compile()
adapter = LangGraphAdapter()
agentcore_agent = adapter.deploy(app, name='DataValidationWorkflow')

4. CrewAI Integration

Multi-agent collaboration

from crewai import Agent, Task, Crew
from bedrock_agentcore import CrewAIAdapter

# Define agents
researcher = Agent(
    role='Researcher',
    goal='Find relevant market data',
    backstory='Expert market analyst'
)

analyst = Agent(
    role='Analyst',
    goal='Analyze market trends',
    backstory='Senior data analyst'
)

# Define tasks
research_task = Task(
    description='Research Q4 market trends',
    agent=researcher
)

analysis_task = Task(
    description='Analyze research findings',
    agent=analyst
)

# Create crew
crew = Crew(
    agents=[researcher, analyst],
    tasks=[research_task, analysis_task]
)

# Deploy to AgentCore
adapter = CrewAIAdapter()
agentcore_crew = adapter.deploy(crew, name='MarketAnalysisCrew')

Data Analysis and Workflow Automation

Code Interpretation

AgentCore provides secure code interpretation for complex analysis:

from bedrock_agentcore import CodeInterpreter

# Create code interpreter
interpreter = CodeInterpreter(
    language='python',
    allowed_libraries=['pandas', 'numpy', 'matplotlib', 'seaborn'],
    max_execution_time=60
)

# Execute analytical code
result = interpreter.execute(
    code="""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load data
df = pd.read_csv(input_data)

# Perform analysis
summary_stats = df.describe()
correlation_matrix = df.corr()

# Create visualizations
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.savefig('correlation_heatmap.png')

# Return results
return {
    'summary': summary_stats.to_dict(),
    'correlations': correlation_matrix.to_dict(),
    'visualization': 'correlation_heatmap.png'
}
    """,
    inputs={
        'input_data': 's3://data-bucket/sales_data.csv'
    },
    outputs_bucket='s3://results-bucket/analysis/'
)

print(result['summary'])
print(f"Visualization saved to: {result['visualization_url']}")

Workflow Automation Examples

Example 1: Document Processing Pipeline

from bedrock_agentcore import WorkflowBuilder

# Build document processing workflow
workflow = WorkflowBuilder(name='DocumentProcessing')

# Step 1: Extract text
workflow.add_step(
    'extract_text',
    agent_type='textract_extractor',
    input='s3://documents/incoming/',
    output='s3://documents/extracted/'
)

# Step 2: Classify documents
workflow.add_step(
    'classify',
    agent_type='document_classifier',
    depends_on='extract_text',
    model='anthropic.claude-3-5-sonnet-20241022-v2:0',
    classes=['invoice', 'contract', 'report', 'other']
)

# Step 3: Route by classification
workflow.add_conditional_step(
    'route',
    depends_on='classify',
    conditions={
        'invoice': 'process_invoice',
        'contract': 'process_contract',
        'report': 'process_report',
        'other': 'manual_review'
    }
)

# Deploy workflow
workflow.deploy()

Example 2: Customer Onboarding Automation

from bedrock_agentcore import AutomationWorkflow

# Create onboarding workflow
onboarding = AutomationWorkflow(name='CustomerOnboarding')

# Collect customer information
onboarding.add_step(
    'collect_info',
    agent='data_collection_agent',
    required_fields=['name', 'email', 'company', 'industry']
)

# Verify information
onboarding.add_step(
    'verify',
    agent='verification_agent',
    checks=['email_valid', 'company_exists', 'duplicate_check']
)

# Create accounts
onboarding.add_parallel_steps([
    ('create_crm_account', 'salesforce_agent'),
    ('create_billing_account', 'billing_agent'),
    ('setup_workspace', 'workspace_agent')
])

# Send welcome email
onboarding.add_step(
    'send_welcome',
    agent='email_agent',
    template='welcome_email',
    wait_for_all_parallel=True
)

# Start workflow
onboarding.execute(input_data={
    'name': 'John Doe',
    'email': 'john@example.com',
    'company': 'Acme Corp'
})

AgentCore APIs

Create Agent

import boto3

agentcore = boto3.client('bedrock-agentcore')

response = agentcore.create_agent(
    agentName='DataAnalysisAgent',
    agentType='CUSTOM',  # or 'FRAMEWORK_BASED'
    framework='strands',  # Optional: strands, langchain, langgraph, crewai
    runtime={
        'language': 'python3.11',
        'timeout': 300,
        'memory': 2048
    },
    codeSource={
        'type': 'S3',
        's3Location': {
            'bucket': 'agent-code-bucket',
            'key': 'agent.zip'
        }
    },
    roleArn='arn:aws:iam::ACCOUNT:role/AgentCoreRole',
    securityConfig={
        'networkMode': 'VPC',
        'vpcConfig': {
            'subnetIds': ['subnet-123', 'subnet-456'],
            'securityGroupIds': ['sg-789']
        },
        'encryptionConfig': {
            'kmsKeyId': 'arn:aws:kms:us-east-1:ACCOUNT:key/KEY_ID'
        }
    }
)

print(f"Agent created: {response['agentId']}")

Invoke Agent

response = agentcore.invoke_agent(
    agentId='agent-123',
    input={
        'query': 'Analyze customer churn data',
        'parameters': {
            'time_period': 'Q4_2024',
            'segments': ['enterprise', 'smb']
        }
    },
    sessionId='session-456',
    enableTrace=True
)

# Process response
print(response['output'])
print(response['executionMetrics'])

Update Agent

response = agentcore.update_agent(
    agentId='agent-123',
    codeSource={
        's3Location': {
            'bucket': 'agent-code-bucket',
            'key': 'agent-v2.zip'
        }
    },
    runtime={
        'memory': 4096  # Increase memory
    }
)

Delete Agent

response = agentcore.delete_agent(
    agentId='agent-123',
    deleteData=False  # Preserve execution logs
)

Integration Patterns

Pattern 1: AgentCore + Bedrock Agents

Combine AgentCore for complex processing with Bedrock Agents for orchestration:

from bedrock_agentcore import AgentCore
import boto3

agentcore = AgentCore()
bedrock = boto3.client('bedrock')

# Create AgentCore agent for complex analysis
analysis_agent = agentcore.create_agent(
    name='ComplexAnalysisAgent',
    framework='langchain',
    code_source='s3://code/analysis.zip'
)

# Create Bedrock Agent as orchestrator
orchestrator = bedrock.create_agent(
    agentName='Orchestrator',
    foundationModel='anthropic.claude-3-5-sonnet-20241022-v2:0',
    instruction='Coordinate analysis tasks and user interactions'
)

# Create action group that calls AgentCore agent
bedrock.create_action_group(
    agentId=orchestrator['agentId'],
    actionGroupName='AnalysisActions',
    actionGroupExecutor={
        'customControl': {
            'endpoint': f'https://agentcore.{region}.amazonaws.com/agent/{analysis_agent["agentId"]}'
        }
    }
)

Pattern 2: AgentCore + Step Functions

Use Step Functions for workflow orchestration with AgentCore for execution:

import boto3

sfn = boto3.client('stepfunctions')

# Define Step Functions state machine
state_machine_definition = {
    'Comment': 'Data processing workflow with AgentCore',
    'StartAt': 'IngestData',
    'States': {
        'IngestData': {
            'Type': 'Task',
            'Resource': 'arn:aws:states:::bedrock-agentcore:invokeAgent',
            'Parameters': {
                'AgentId': 'ingest-agent-id',
                'Input.$': '$.inputData'
            },
            'Next': 'ProcessData'
        },
        'ProcessData': {
            'Type': 'Task',
            'Resource': 'arn:aws:states:::bedrock-agentcore:invokeAgent',
            'Parameters': {
                'AgentId': 'processing-agent-id',
                'Input.$': '$.IngestData.output'
            },
            'Next': 'GenerateReport'
        },
        'GenerateReport': {
            'Type': 'Task',
            'Resource': 'arn:aws:states:::bedrock-agentcore:invokeAgent',
            'Parameters': {
                'AgentId': 'reporting-agent-id',
                'Input.$': '$.ProcessData.output'
            },
            'End': True
        }
    }
}

# Create state machine
response = sfn.create_state_machine(
    name='DataProcessingWorkflow',
    definition=json.dumps(state_machine_definition),
    roleArn='arn:aws:iam::ACCOUNT:role/StepFunctionsRole'
)

Pattern 3: AgentCore + EventBridge

Event-driven agent execution:

import boto3

events = boto3.client('events')

# Create EventBridge rule
rule = events.put_rule(
    Name='NewDocumentRule',
    EventPattern=json.dumps({
        'source': ['aws.s3'],
        'detail-type': ['Object Created'],
        'detail': {
            'bucket': {'name': ['document-bucket']}
        }
    }),
    State='ENABLED'
)

# Add AgentCore agent as target
events.put_targets(
    Rule='NewDocumentRule',
    Targets=[{
        'Id': '1',
        'Arn': f'arn:aws:bedrock-agentcore:us-east-1:ACCOUNT:agent/document-processor',
        'RoleArn': 'arn:aws:iam::ACCOUNT:role/EventBridgeRole',
        'Input': json.dumps({
            'agentId': 'document-processor-id',
            'inputTransform': {
                'documentPath': '$.detail.object.key'
            }
        })
    }]
)

Security and Compliance

IAM Policies for AgentCore

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "AgentCoreManagement",
      "Effect": "Allow",
      "Action": [
        "bedrock-agentcore:CreateAgent",
        "bedrock-agentcore:UpdateAgent",
        "bedrock-agentcore:DeleteAgent",
        "bedrock-agentcore:GetAgent",
        "bedrock-agentcore:ListAgents",
        "bedrock-agentcore:InvokeAgent"
      ],
      "Resource": "arn:aws:bedrock-agentcore:*:ACCOUNT:agent/*"
    },
    {
      "Sid": "CodeAccess",
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:ListBucket"
      ],
      "Resource": [
        "arn:aws:s3:::agent-code-bucket",
        "arn:aws:s3:::agent-code-bucket/*"
      ]
    },
    {
      "Sid": "ExecutionRole",
      "Effect": "Allow",
      "Action": [
        "iam:PassRole"
      ],
      "Resource": "arn:aws:iam::ACCOUNT:role/AgentExecutionRole"
    }
  ]
}

Encryption

# Create agent with encryption
agent = agentcore.create_agent(
    name='SecureAgent',
    encryptionConfig={
        'kmsKeyId': 'arn:aws:kms:us-east-1:ACCOUNT:key/KEY_ID',
        'encryptionType': 'AWS_MANAGED',  # or 'CUSTOMER_MANAGED'
        'encryptDataAtRest': True,
        'encryptDataInTransit': True
    }
)

Audit Logging

# Enable comprehensive logging
agentcore.update_agent(
    agentId='agent-123',
    loggingConfig={
        'cloudWatchLogs': {
            'enabled': True,
            'logGroupName': '/aws/bedrock-agentcore/agent-123',
            'logLevel': 'DEBUG'  # DEBUG, INFO, WARN, ERROR
        },
        'cloudTrail': {
            'enabled': True
        },
        's3Logs': {
            'enabled': True,
            'bucket': 'audit-logs-bucket',
            'prefix': 'agentcore/'
        }
    }
)

Best Practices

1. Code Organization

# Recommended project structure
"""
my-agentcore-project/
├── agents/
│   ├── __init__.py
│   ├── data_analyzer.py
│   ├── workflow_orchestrator.py
│   └── report_generator.py
├── tools/
│   ├── __init__.py
│   ├── data_tools.py
│   └── api_tools.py
├── config/
│   ├── dev.yaml
│   ├── prod.yaml
│   └── sandbox_config.yaml
├── tests/
│   ├── test_agents.py
│   └── test_tools.py
├── requirements.txt
└── deploy.py
"""

2. Error Handling

from bedrock_agentcore import AgentCore, AgentError
import time

def invoke_with_retry(agent_id, input_data, max_retries=3):
    """Invoke agent with exponential backoff retry"""

    for attempt in range(max_retries):
        try:
            response = agentcore.invoke_agent(
                agentId=agent_id,
                input=input_data
            )
            return response

        except AgentError as e:
            if e.code == 'ThrottlingException' and attempt < max_retries - 1:
                wait_time = 2 ** attempt
                print(f"Throttled, retrying in {wait_time}s...")
                time.sleep(wait_time)
            else:
                raise

3. Performance Optimization

# Use caching for repeated operations
from bedrock_agentcore import CacheManager

cache = CacheManager(
    ttl_seconds=3600,
    max_size_mb=100
)

@cache.cached()
def expensive_analysis(data_path):
    """Cache results of expensive operations"""
    # Perform analysis
    return results

4. Monitoring

from bedrock_agentcore import MetricsPublisher

metrics = MetricsPublisher(namespace='MyAgents')

# Publish custom metrics
metrics.put_metric(
    metric_name='AnalysisLatency',
    value=response_time_ms,
    unit='Milliseconds',
    dimensions=[
        {'Name': 'AgentId', 'Value': agent_id},
        {'Name': 'Environment', 'Value': 'production'}
    ]
)

Production Deployment

Deployment Checklist

Code tested in sandbox environment
IAM roles and policies configured
Encryption enabled for data at rest and in transit
Logging and monitoring configured
Resource limits set appropriately
Backup and disaster recovery plan in place
Security scan completed
Load testing performed
Documentation updated

Blue-Green Deployment

from bedrock_agentcore import DeploymentManager

deployer = DeploymentManager()

# Create new version (green)
new_version = deployer.create_version(
    agent_id='agent-123',
    code_source='s3://code/agent-v2.zip',
    description='Version 2.0 with improved analysis'
)

# Test new version
test_results = deployer.run_tests(
    agent_id='agent-123',
    version=new_version,
    test_suite='integration_tests'
)

if test_results['success_rate'] > 0.95:
    # Switch traffic to new version
    deployer.update_alias(
        agent_id='agent-123',
        alias='production',
        version=new_version
    )
    print("✓ Deployment successful")
else:
    # Rollback
    deployer.delete_version(
        agent_id='agent-123',
        version=new_version
    )
    print("✗ Deployment failed, rolled back")

Cost Optimization

# Monitor and optimize costs
from bedrock_agentcore import CostAnalyzer

analyzer = CostAnalyzer()

# Get cost breakdown
costs = analyzer.get_monthly_costs(
    agent_id='agent-123',
    month='2025-03'
)

print(f"Compute: ${costs['compute']}")
print(f"Storage: ${costs['storage']}")
print(f"Data Transfer: ${costs['data_transfer']}")

# Get optimization recommendations
recommendations = analyzer.get_recommendations(
    agent_id='agent-123'
)

for rec in recommendations:
    print(f"{rec['type']}: {rec['description']}")
    print(f"Estimated savings: ${rec['estimated_savings']}/month")

Conclusion

Amazon Bedrock AgentCore provides a powerful, secure, and flexible platform for building enterprise-grade AI agents. With support for multiple frameworks, secure sandbox execution, and comprehensive AWS integration, AgentCore simplifies agent development while maintaining enterprise security and compliance requirements.

Free Trial: Available until September 16, 2025 Getting Started: https://docs.aws.amazon.com/bedrock/agentcore/

Last Updated: March 2025 Status: Generally Available

Amazon Bedrock AgentCore: Comprehensive Technical Guide

Amazon Bedrock AgentCore: Comprehensive Technical Guide

Table of Contents

Introduction to AgentCore

Key Benefits

Use Cases

Core Features

1. Secure Serverless Runtime

2. Framework Support

3. Data Analysis Capabilities

4. Workflow Automation

AgentCore Architecture

System Architecture

Component Overview

Getting Started

Prerequisites

Installation

Quick Start Example

Secure Sandbox Runtime

Runtime Isolation

Language Runtime Support

Resource Management

Supported Frameworks

1. Strands Agents SDK

2. LangChain Integration

3. LangGraph Integration

4. CrewAI Integration

Data Analysis and Workflow Automation

Code Interpretation

Workflow Automation Examples

AgentCore APIs

Create Agent

Invoke Agent

Update Agent

Delete Agent

Integration Patterns

Pattern 1: AgentCore + Bedrock Agents

Pattern 2: AgentCore + Step Functions

Pattern 3: AgentCore + EventBridge

Security and Compliance

IAM Policies for AgentCore

Encryption

Audit Logging

Best Practices

1. Code Organization

2. Error Handling

3. Performance Optimization

4. Monitoring

Production Deployment

Deployment Checklist

Blue-Green Deployment

Cost Optimization

Conclusion