aws-agentcore

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Build AI agents with AWS Bedrock AgentCore. Use when developing agents on AWS infrastructure, creating tool-use patterns, implementing agent orchestration, or integrating with Bedrock models. Triggers on keywords like AgentCore, Bedrock Agent, AWS agent, Lambda tools.

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NPX Install

npx skill4agent add hoodini/ai-agents-skills aws-agentcore

Tags

Translated version includes tags in frontmatter
aws-bedrock-agentcoreai-agent-developmentlambda-tool-integrationagent-orchestrationbedrock-model-integration

SKILL.md Content

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AWS Bedrock AgentCore

Build production-grade AI agents on AWS infrastructure.

Quick Start

python
import boto3
from agentcore import Agent, Tool

# Initialize AgentCore client
client = boto3.client('bedrock-agent-runtime')

# Define a tool
@Tool(name="search_database", description="Search the product database")
def search_database(query: str, limit: int = 10) -> dict:
    # Tool implementation
    return {"results": [...]}

# Create agent
agent = Agent(
    model_id="anthropic.claude-3-sonnet",
    tools=[search_database],
    instructions="You are a helpful product search assistant."
)

# Invoke agent
response = agent.invoke("Find laptops under $1000")

AgentCore Components

AgentCore provides these primitives:
ComponentPurpose
RuntimeServerless agent execution (framework-agnostic)
GatewayConvert APIs/Lambda to MCP-compatible tools
MemoryMulti-strategy memory (semantic, user preference)
IdentityAuth with Cognito, Okta, Google, EntraID
ToolsCode Interpreter, Browser Tool
ObservabilityDeep analysis and tracing

Lambda Tool Integration

python
# Lambda function as tool
import json

def lambda_handler(event, context):
    action = event.get('actionGroup')
    function = event.get('function')
    parameters = event.get('parameters', [])
    
    # Parse parameters
    params = {p['name']: p['value'] for p in parameters}
    
    if function == 'get_weather':
        result = get_weather(params['city'])
    elif function == 'book_flight':
        result = book_flight(params['origin'], params['destination'])
    
    return {
        'response': {
            'actionGroup': action,
            'function': function,
            'functionResponse': {
                'responseBody': {
                    'TEXT': {'body': json.dumps(result)}
                }
            }
        }
    }

Agent Orchestration

python
from agentcore import SupervisorAgent, SubAgent

# Create specialized sub-agents
research_agent = SubAgent(
    name="researcher",
    model_id="anthropic.claude-3-sonnet",
    instructions="You research and gather information."
)

writer_agent = SubAgent(
    name="writer", 
    model_id="anthropic.claude-3-sonnet",
    instructions="You write clear, engaging content."
)

# Create supervisor
supervisor = SupervisorAgent(
    model_id="anthropic.claude-3-opus",
    sub_agents=[research_agent, writer_agent],
    routing_strategy="supervisor"  # or "intent_classification"
)

response = supervisor.invoke("Write a blog post about AI agents")

Guardrails Integration

python
from agentcore import Agent, Guardrail

# Define guardrail
guardrail = Guardrail(
    guardrail_id="my-guardrail-id",
    guardrail_version="1"
)

agent = Agent(
    model_id="anthropic.claude-3-sonnet",
    guardrails=[guardrail],
    tools=[...],
)

AgentCore Gateway

Convert existing APIs to MCP-compatible tools:
python
# gateway_setup.py
from bedrock_agentcore import GatewayClient

gateway = GatewayClient()

# Create gateway from OpenAPI spec
gateway.create_target(
    name="my-api",
    type="OPENAPI",
    openapi_spec_path="./api-spec.yaml"
)

# Create gateway from Lambda function
gateway.create_target(
    name="my-lambda-tool",
    type="LAMBDA",
    function_arn="arn:aws:lambda:us-east-1:123456789:function:my-tool"
)

AgentCore Memory

python
from agentcore import Agent, Memory

# Create memory with multiple strategies
memory = Memory(
    name="customer-support-memory",
    strategies=["semantic", "user_preference"]
)

agent = Agent(
    model_id="anthropic.claude-3-sonnet",
    memory=memory,
    tools=[...],
)

# Memory persists across sessions
response = agent.invoke(
    "What did we discuss last time?",
    session_id="user-123"
)

Official Use Cases Repository

AWS provides production-ready implementations:
Repository: https://github.com/awslabs/amazon-bedrock-agentcore-samples

Available Use Cases (
02-use-cases/
)

Use CaseDescription
A2A Multi-Agent Incident ResponseAgent-to-Agent with Strands + OpenAI SDK
Customer Support AssistantMemory, Knowledge Base, Google OAuth
Market Trends AgentLangGraph with browser tools
DB Performance AnalyzerPostgreSQL integration
Device Management AgentIoT with Cognito auth
Enterprise Web IntelligenceBrowser tools for research
Text to Python IDEAgentCore Code Interpreter
Video Games Sales AssistantAmplify + CDK deployment

Quick Start with Use Cases

bash
git clone https://github.com/awslabs/amazon-bedrock-agentcore-samples.git
cd amazon-bedrock-agentcore-samples/02-use-cases/customer-support-assistant
# Follow README for deployment

Resources