Agent Frameworks & Patterns¶

2-Week Nanodegree Module¶

Module Overview¶

In an era where software systems must be intelligent, adaptive, and capable of autonomous decision-making, traditional programming approaches are reaching their limits. Data pipelines now need to handle ambiguous requirements, APIs must reason about context, and systems must adapt to changing conditions without constant human intervention. This self-paced 2-week module provides comprehensive instruction on building production-ready AI agents using 2025 state-of-the-art frameworks and implementing the five core agentic patterns: Reflection, Tool Use, ReAct, Planning, and Multi-Agent Collaboration. Through hands-on tutorials and progressive implementation, you'll master cutting-edge agent development with performance breakthroughs (CrewAI 5.76x faster execution), distributed system production capabilities, and modern protocol standardization (MCP, A2A, ACP, ANP). You'll develop skills to create intelligent, adaptable agents using both bare-metal approaches and established frameworks optimized for 2025 production environments.

Agent Frameworks Architecture

Prerequisites¶

Python programming experience (intermediate level)
Basic understanding of LLMs and API interactions
Familiarity with JSON/REST APIs and HTTP protocols
Experience with virtual environments and package management
Understanding of software design patterns and object-oriented programming

Week 1: Core Agent Patterns & Framework Foundations¶

The first week focuses on building a solid foundation in agent architecture and core patterns. You'll start by understanding the theoretical framework that underpins all modern agent systems, then dive deep into implementations that reveal how intelligent systems actually work. This progression from theory to practice ensures you can both architect new systems and debug existing ones.

Session 0: Introduction to Agent Frameworks & Patterns (Self-Study)¶

Before jumping into code, understanding the landscape of possibilities is crucial for making informed architectural decisions. This session provides the strategic foundation that will guide your framework choices throughout your agent development career.

Content: Understanding the five core agentic patterns, framework landscape, and architectural considerations Materials: Session0_Introduction_to_Agent_Frameworks_Patterns.md Self-Check: 15-question multiple choice quiz covering agentic patterns and framework concepts

Session 1: Bare Metal Agents (Pure Python Implementation)¶

Building agents from scratch isn't just an academic exercise - it's the key to understanding what happens when your production systems fail, when you need to optimize performance, or when you need to implement custom behavior that frameworks don't support. This deep dive into first principles will make you a more effective agent developer regardless of which frameworks you ultimately choose.

Content: Building agents from scratch using pure Python, understanding core agent mechanics, real LLM integration, dependency injection patterns, and production case study analysis Materials: Session1_Bare_Metal_Agents.md + Session1_Bare_Metal_Agents-solution.md + Session1_ModuleD_Coding_Assistant_Case_Study.md Self-Check: Multiple choice quiz covering agent fundamentals, LLM integration, and implementation patterns

Session 2: LangChain Foundations & Tool Integration¶

LangChain transformed the agent development landscape by making powerful patterns accessible to developers. Understanding its abstractions and design philosophy is essential for modern agent development, as many other frameworks either build upon or react against its architectural decisions.

Content: LangChain architecture, agent types, tool ecosystem integration, LangGraph integration patterns, and production deployment strategies Materials: Session2_LangChain_Foundations.md + Session2_LangChain_Foundations-solution.md Self-Check: Multiple choice quiz covering LangChain agents, LangGraph integration, and production deployment patterns

Session 3: LangGraph Multi-Agent Workflows¶

Linear chains of reasoning have limitations when dealing with complex, branching logic and parallel processing requirements. Graph-based orchestration opens up architectural possibilities that are essential for sophisticated agent systems that need to handle multiple concurrent tasks and conditional workflows.

Content: Building complex workflows with LangGraph, state management, agent orchestration, orchestrator-worker patterns, and production-grade features for distributed systems Materials: Session3_LangGraph_Multi_Agent_Workflows.md + Session3_LangGraph_Multi_Agent_Workflows-solution.md Self-Check: Multiple choice quiz covering workflow patterns, orchestrator-worker architectures, and production state management

Session 4: CrewAI Team Orchestration¶

Real-world problems often require different types of expertise working together. CrewAI pioneered team-based agent coordination, making it possible to create specialized agents that collaborate effectively - much like how human teams combine individual expertise to solve complex challenges.

Content: Hierarchical multi-agent systems, role-based collaboration, task delegation, CrewAI Flows for advanced workflows, and performance advantages (5.76x execution speed improvements) Materials: Session4_CrewAI_Team_Orchestration.md + Session4_CrewAI_Team_Orchestration-solution.md Self-Check: Multiple choice quiz covering team orchestration, CrewAI Flows, and performance optimization patterns

Session 5: PydanticAI Type-Safe Agents¶

As agents interact with critical business systems, type safety becomes essential for reliability and maintainability. PydanticAI brings the discipline of type checking to agent development, helping you catch errors before they reach production and making your agent systems more predictable and debuggable.

Content: Type-safe agent development, structured outputs, validation patterns, streaming validation capabilities, and multi-provider support for production deployments Materials: Session5_PydanticAI_Type_Safe_Agents.md + Session5_PydanticAI_Type_Safe_Agents-solution.md Self-Check: Multiple choice quiz covering type safety, streaming validation, and multi-provider agent architectures

Session 6: Atomic Agents Modular Architecture¶

As agent systems grow in complexity, modular design becomes crucial for maintainability and scalability. Atomic Agents represents a different architectural philosophy - instead of monolithic frameworks, it provides composable building blocks that can be combined to create exactly the system you need.

Content: Modular, composable agent architecture with atomic components, lightweight patterns, composable tool systems, and modular multi-agent coordination bridging individual frameworks and production patterns Materials: Session6_Atomic_Agents_Modular_Architecture.md + Session6_Atomic_Agents_Modular_Architecture-solution.md Self-Check: Multiple choice quiz covering atomic patterns, component composition, and modular architecture design

Week 2: Production Frameworks & System Integration¶

The second week focuses on production-ready solutions and enterprise integration challenges. Here you'll explore frameworks specifically designed for production environments and learn how to integrate agent systems into existing enterprise infrastructure. This is where theoretical knowledge becomes practical expertise.

Session 7: ADK Production Agent Development¶

Google's Agent Development Kit represents enterprise-grade thinking about agent development. Built from the ground up for production environments, ADK provides the opinionated structure and best practices that come from operating agent systems at massive scale.

Content: Google's Agent Development Kit for production-ready agent systems Materials: Session7_First_ADK_Agent.md + Session7_First_ADK_Agent-solution.md Self-Check: Multiple choice quiz covering ADK architecture and production patterns

Session 8: Agno Production-Ready Agents¶

The gap between development and production is where many agent projects fail. Agno addresses the operational challenges of running agent systems in production - monitoring, scaling, error handling, and integration with existing infrastructure.

Content: Agno framework for scalable agent deployment, advanced monitoring, production integration patterns, and production-grade observability systems Materials: Session8_Agno_Production_Ready_Agents.md + Session8_Agno_Production_Ready_Agents-solution.md Self-Check: Multiple choice quiz covering production deployment, advanced monitoring, and production observability

Session 9: Multi-Agent Patterns & ReAct Implementation¶

At the cutting edge of agent development are systems where multiple agents coordinate to solve problems that no single agent could handle. These patterns represent the future of autonomous systems and require sophisticated understanding of distributed coordination and communication protocols.

Content: Advanced multi-agent architectures, ReAct reasoning patterns, coordination strategies, production communication protocols, and production-grade pattern implementations Materials: Session9_Multi_Agent_Patterns_ReAct.md + Session9_Multi_Agent_Patterns_ReAct-solution.md Self-Check: Multiple choice quiz covering production patterns, advanced reasoning, and production coordination strategies

Session 10: Production Integration & Deployment¶

The ultimate test of any agent system is successful deployment and operation in production environments. This session covers the practical engineering challenges of scaling, monitoring, and maintaining agent systems in enterprise contexts.

Content: Advanced containerization, cloud deployment, production monitoring, modern protocol integration (MCP, A2A, ACP, ANP), performance benchmarking, and production-grade scaling strategies Materials: Session10_Enterprise_Integration_Production_Deployment.md + Session10_Enterprise_Integration_Production_Deployment-solution.md Self-Check: Multiple choice quiz covering advanced deployment, protocol integration, and production scaling patterns

Capstone Project: Multi-Framework Agent Ecosystem¶

The capstone project integrates everything you've learned into a comprehensive demonstration of modern agent development capabilities. This isn't just an academic exercise - it's designed to showcase the kind of sophisticated, production-ready agent system that represents the current state of the art.

Project Overview: Build a comprehensive 2025 state-of-the-art agent system demonstrating all five agentic patterns using multiple frameworks with modern protocol integration

Requirements¶

Implement Reflection pattern for self-improvement with production observability
Create Tool Use agents with external API integration and MCP protocol support
Build ReAct agents for complex reasoning chains with performance optimization
Develop Planning agents for multi-step task execution using orchestrator-worker patterns
Deploy Multi-Agent systems with hierarchical coordination and A2A/ACP protocols
Design atomic agent components using modular architecture patterns
Implement composable tool systems with plug-and-play component integration
Integrate with production systems using modern monitoring and protocol standards
Demonstrate performance improvements (measure against CrewAI's 5.76x benchmark)
Implement streaming validation and multi-provider support

Deliverables¶

Complete source code with comprehensive documentation and 2025 best practices
Working deployment with advanced monitoring, observability, and protocol integration
Architecture documentation and 2025 framework comparison analysis
Performance benchmarks demonstrating 2025 optimizations and improvements
Production integration patterns with modern protocol implementations
Production-ready deployment with container orchestration and scaling strategies

2025 Technology Stack & Performance Benchmarks¶

Understanding the current landscape of agent technologies is crucial for making informed architectural decisions. The field is evolving rapidly, with significant performance improvements and new protocols emerging regularly.

Modern Protocol Standards¶

Model Context Protocol (MCP): Standardized LLM-application integration
Agent-to-Agent (A2A): Direct inter-agent communication protocols
Agent Communication Protocol (ACP): Production agent coordination
Agent Network Protocol (ANP): Multi-system agent federation

Performance Breakthroughs¶

CrewAI Performance: 5.76x faster execution with new flow architecture
LangGraph Optimizations: Enhanced state management and parallel processing
PydanticAI Streaming: Real-time validation with multi-provider support
Production Observability: Advanced monitoring with OpenTelemetry integration

2025 Framework Comparison Matrix¶

Framework	Performance	Production Ready	Learning Curve	Production Focus	2025 Features
LangChain	Good	⭐⭐⭐	Moderate	High	LangGraph integration, enhanced tools
LangGraph	Excellent	⭐⭐⭐⭐	Moderate	Very High	Orchestrator patterns, production state
CrewAI	Outstanding	⭐⭐⭐⭐	Easy	Very High	5.76x speed, Flows architecture
PydanticAI	Very Good	⭐⭐⭐⭐⭐	Easy	Very High	Streaming validation, multi-provider
Atomic Agents	Excellent	⭐⭐⭐⭐	Easy	High	Modular components, lightweight patterns
Google ADK	Excellent	⭐⭐⭐⭐⭐	Moderate	Extreme	Production integration, Google Cloud
Agno	Good	⭐⭐⭐⭐	Moderate	High	Production monitoring, observability

Production Integration Technologies¶

Container Orchestration: Kubernetes with Helm charts
Monitoring Stack: Prometheus, Grafana, OpenTelemetry, Jaeger
Message Queues: Redis Streams, Apache Kafka for agent communication
Database Integration: PostgreSQL, MongoDB with connection pooling
API Gateways: Kong, Ambassador for agent service mesh
Security: OAuth 2.0, JWT tokens, rate limiting, audit logging

Comprehensive Resource Library¶

Core Documentation¶

Video Tutorials¶

Hands-on Tutorials¶

Advanced Reading¶

Code Repositories¶

Learning Outcomes¶

By completion, you will have transformed from understanding basic agent concepts to becoming an architect capable of designing and implementing sophisticated, production-ready intelligent systems. These outcomes represent the skills needed to lead agent development initiatives in modern organizations.

Technical Skills¶

Implement all five agentic patterns (Reflection, Tool Use, ReAct, Planning, Multi-Agent) with 2025 best practices
Build agents using multiple frameworks with performance optimization and appropriate framework selection
Design multi-agent systems with orchestrator-worker patterns and enterprise communication protocols
Deploy production-ready agents with advanced monitoring, observability, and streaming capabilities
Integrate agents with enterprise systems using modern protocols (MCP, A2A, ACP, ANP) and external APIs
Optimize agent performance through benchmarking, profiling, and 2025 performance enhancements
Implement multi-provider support for enterprise flexibility and vendor independence
Apply performance breakthroughs including CrewAI's 5.76x execution improvements

Architectural Understanding¶

Compare 2025 framework trade-offs including performance characteristics and enterprise readiness
Design scalable agent architectures for production environments with modern protocol support
Implement proper separation of concerns between agents, tools, orchestration, and enterprise integration layers
Plan for fault tolerance and recovery in multi-agent systems with advanced observability
Apply enterprise integration patterns for production-grade system architecture
Understand protocol standardization and its impact on agent system design

Industry Readiness¶

Follow 2025 framework best practices and cutting-edge design patterns
Debug complex agent interactions using advanced observability and monitoring tools
Monitor and maintain production agent systems with enterprise-grade tooling
Evaluate and select appropriate frameworks based on 2025 performance benchmarks and enterprise requirements
Deploy enterprise-grade solutions with proper scaling, monitoring, and protocol integration
Apply performance optimization techniques and modern architectural patterns

Self-Assessment Structure¶

Knowledge Checks¶

Each session includes multiple choice quizzes designed to verify not just memorization, but deep understanding of key concepts and their practical implications:

Session 0: 15 questions covering agentic patterns and framework fundamentals
Sessions 1-10: 10-12 questions per session covering specific implementation topics
Capstone Project: Self-evaluation checklist for project completeness

Practical Validation¶

Code Examples: Each session provides working code examples with solutions
Progressive Complexity: Build upon previous sessions for comprehensive learning
Framework Comparison: Compare implementations across different frameworks
Self-Check Activities: Verify implementations work as expected

Progress Tracking¶

Complete all session materials and quizzes to demonstrate mastery
Successfully implement and test all code examples
Build and deploy the capstone project following provided specifications

Development Environment Setup¶

Required Software¶

# Python environment (3.11+)
python3.11+

# Core framework dependencies (2025 versions)
pip install langchain langchain-community langchain-openai
pip install langgraph
pip install crewai crewai-tools
pip install pydantic-ai
pip install google-adk

# Enterprise and production tools
pip install fastapi uvicorn
pip install pytest pytest-asyncio
pip install prometheus-client

# 2025 Protocol and Integration Support
pip install mcp-client  # Model Context Protocol
pip install asyncio-pool  # Performance optimization
pip install opentelemetry-api opentelemetry-sdk  # Advanced observability
pip install redis  # State management
pip install kubernetes  # Container orchestration

Recommended Tools¶

IDE: VS Code with Python extension and Jupyter support
API Testing: Postman or curl for testing agent endpoints
Monitoring: Prometheus + Grafana for production monitoring
Version Control: Git with proper branching strategy
Containers: Docker for deployment and isolation

Development Workflow¶

Local Development: Use file-based persistence for rapid iteration
Integration Testing: Test agent interactions in isolated environments
Production Deployment: Container-based deployment with proper monitoring

Completion Requirements¶

To successfully complete this nanodegree module:

Study all session materials (Sessions 0-10)
Complete all multiple choice quizzes with satisfactory scores
Successfully implement all code examples and framework integrations
Build and deploy the capstone project demonstrating mastery of all five agentic patterns

Completion Recognition: "Agent Frameworks & Patterns Nanodegree Module Certificate"

Self-Study Support¶

Available Resources¶

Comprehensive Documentation: Each session includes detailed explanations and examples
Solution Code: Complete implementations provided for all exercises
Framework Comparisons: Side-by-side analysis of different approaches
Community Resources: Links to official documentation and developer communities

Learning Path¶

Week 1: Master core patterns through framework-specific implementations including atomic/modular approaches
Week 2: Explore production frameworks and enterprise integration
Capstone: Integrate all patterns in a comprehensive multi-framework system with atomic components

The landscape of software engineering is shifting toward intelligent, autonomous systems. Traditional applications are evolving into adaptive platforms that can reason, plan, and execute complex tasks without constant human oversight. This module provides comprehensive, self-paced instruction on 2025 state-of-the-art agent frameworks and the five core agentic patterns. You'll gain practical experience building intelligent agents that can reflect, use tools, reason, plan, and collaborate effectively in production environments using cutting-edge performance optimizations, modern protocol standards, and enterprise-grade deployment strategies. Master the latest technological advances including CrewAI's 5.76x performance improvements, advanced observability systems, and modern protocol integration (MCP, A2A, ACP, ANP) for enterprise-ready agent systems.

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