Strands Agents SDK를 사용하여 생산-ready AI Agent 구축을위한 종합 고급 과정. 이 저장소에는 도구 통합, 메모리 지속, 모델 Context Protocol (MCP) 및 종합 관찰 가능성 등 고급 기능을 가르치는 6 진보 된 실험실이 포함되어 있습니다.

과정 개요

이 고급 과정은 에이전트 클래스, 에이전트 루프 및 지능형 자율 시스템을 구동하는 구성 요소의 완전한 생태계에 초점을 맞춘 생산 ready AI 에이전트에 기반 및 고급 전문 지식을 제공합니다. 에이전트 AI의 핵심 원칙을 마스터하고, 에이전트가 큰 언어 모델, 도구 및 메모리를 결합하는 방법을 이해하고, 계획 및 행동을 자율적으로 수행 할 수있는 시스템을 만들 수 있습니다.

** 주제:**

• Strands Agents SDK - 고급 에이전트 아키텍처 및 수명주기 관리
• Model Context Protocol (MCP) - 표준화된 도구 및 서비스 통합
• Multi-Provider Configuration - Amazon Bedrock, Anthropic, OpenAI 및 올라마
• Advanced Processing - 후크, 세션 관리 및 대화 전략
• ** 메모리 시스템 ** - FAISS, OpenSearch 및 Mem0과 장기 지속 메모리

Advanced Strands Agents with MCP

A comprehensive advanced course for building production-ready AI agents using the Strands Agents SDK. This repository contains 6 progressive labs that teach advanced capabilities including tool integration, memory persistence, Model Context Protocol (MCP), and comprehensive observability.

🎯 Course Overview

This advanced course provides foundational and advanced expertise in building production-ready AI agents, focusing on the agent class, agentic loop, and the complete ecosystem of components that power intelligent autonomous systems. You’ll master core principles of agentic AI, understanding how agents combine large language models, tools, and memory to create systems that can understand, plan, and execute actions autonomously.

Course Topics:

• Strands Agents SDK - Advanced agent architecture and lifecycle management
• Model Context Protocol (MCP) - Standardized tool and service integration
• Multi-Provider Configuration - Amazon Bedrock, Anthropic, OpenAI, and Ollama
• Advanced Processing - Hooks, session management, and conversation strategies
• Memory Systems - Long-term persistent memory with FAISS, OpenSearch, and Mem0
• Enterprise Features - Observability, metrics analysis, and performance optimization

Prerequisites: Basic understanding of AI/ML concepts. For beginners, we recommend starting with Getting Started with Strands Agents (Course 1).

📚 Lab Structure

Lab 1: Overview of Strands Agents (12:52)

Files: first_agent.py

Learn fundamental agentic AI concepts and build your first Strands agent:

• Basic agent creation with default configuration (no API keys required)
• Core agent components and execution flow
• Agent result examination (message, metrics, state, stop reasons)
• Dynamic model configuration and system prompt modification
• Conversation history management and message clearing

Lab 2: Model Providers and Configuration (11:59)

Files: anthropic_model.py, bedrock_model.py, ollama_model.py, openai_model.py

Configure agents across multiple LLM providers for flexibility and cost optimization:

• Model architecture overview and provider-specific parameters
• Bedrock model setup with structured output capabilities
• Anthropic model configuration with thinking mode
• Ollama local deployment and OpenAI integration
• Metrics analysis and performance monitoring

Lab 3: Advanced Response Processing with Hooks (13:30)

Files: async_example.py, hook_example_1.py, hook_example_2.py

Implement custom logic to intercept and modify agent behavior at lifecycle points:

• Event-driven hook system and lifecycle management
• Before/after event handling and agent modifications
• Async iterators, callback handlers, and retry logic
• Tool hook examples and precision parameter setup

Lab 4: Tools and MCP Integration (18:55)