Aws agentcore

Skill Makiya1202/ai-agents-skills/skills/aws-agentcore

🧠 Enhance AI agents with a collection of skills for improved coding assistance, enabling efficient and production-ready solutions.

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npx -y skills add Makiya1202/ai-agents-skills --skill 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.

SKILL.md

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AWS Bedrock AgentCore

Build production-grade AI agents on AWS infrastructure.

Quick Start

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:

Component	Purpose
Runtime	Serverless agent execution (framework-agnostic)
Gateway	Convert APIs/Lambda to MCP-compatible tools
Memory	Multi-strategy memory (semantic, user preference)
Identity	Auth with Cognito, Okta, Google, EntraID
Tools	Code Interpreter, Browser Tool
Observability	Deep analysis and tracing

Lambda Tool Integration

# 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

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

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:

# 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

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 Case	Description
A2A Multi-Agent Incident Response	Agent-to-Agent with Strands + OpenAI SDK
Customer Support Assistant	Memory, Knowledge Base, Google OAuth
Market Trends Agent	LangGraph with browser tools
DB Performance Analyzer	PostgreSQL integration
Device Management Agent	IoT with Cognito auth
Enterprise Web Intelligence	Browser tools for research
Text to Python IDE	AgentCore Code Interpreter
Video Games Sales Assistant	Amplify + CDK deployment

Quick Start with Use Cases

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

Official Samples: https://github.com/awslabs/amazon-bedrock-agentcore-samples
Use Cases: https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/02-use-cases
Tutorials: https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/01-tutorials