Skip to content

Instantly share code, notes, and snippets.

@bgauryy

bgauryy/AGENTIC_AI_LEARNING_PATH.md

Created November 9, 2025 08:27
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save bgauryy/6e479faec9a75673821d011a7f102a1f to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save bgauryy/6e479faec9a75673821d011a7f102a1f to your computer and use it in GitHub Desktop.
Download ZIP
Octocode AI Research Summary On - github. com/panaversity/learn-agentic-ai
Raw
AGENTIC_AI_LEARNING_PATH.md

Comprehensive Agentic AI Learning Path & Research

Table of Contents

  1. Executive Summary
  2. Core AI Topics Covered
  3. Learning Path Structure
  4. Best Practices & Design Patterns
  5. Additional Learning Resources
  6. Certification Path
  7. Recommended Repository Collection

Executive Summary

This comprehensive guide is based on research of the Panaversity Learn Agentic AI program, which focuses on the Dapr Agentic Cloud Ascent (DACA) design pattern. The program addresses the critical challenge: "How do we design AI Agents that can handle 10 million concurrent AI Agents without failing?"

Key Technologies Stack:

  • Core Framework: OpenAI Agents SDK (Python-first, minimal abstraction)
  • Cloud Infrastructure: Kubernetes + Dapr + Ray
  • Protocols: Model Context Protocol (MCP) + Agent-to-Agent (A2A) + NANDA
  • Memory Systems: LangMem & mem0
  • Deployment: Azure Container Apps, Kubernetes
  • Additional: FastAPI, PostgreSQL, Redis, CockroachDB, RabbitMQ

Why This Approach?

The DACA pattern promotes:

  • AI-First Development: Building with agentic capabilities from the ground up
  • Cloud-Native Architecture: Stateless, containerized, infinitely scalable
  • Standardized Communication: MCP for tools, A2A for inter-agent collaboration
  • Planet-Scale Readiness: Free-tier friendly with self-hosted LLM support

Core AI Topics Covered

1. Agentic AI Fundamentals

  • Agent architecture and design
  • Agent lifecycle and execution loops
  • Tool integration and function calling
  • Multi-agent collaboration and handoffs
  • Context management and memory systems
  • Prompt engineering and Chain-of-Thought reasoning

2. OpenAI Agents SDK (Primary Framework)

  • Python-first orchestration
  • Core primitives: Agents, Tools, Handoffs, Guardrails
  • Structured outputs with Pydantic
  • Streaming and async patterns
  • Agent cloning and dynamic instructions
  • Session memory management

3. AI Protocols

  • HTTP/REST: Standard web protocols
  • Streamable HTTP: Real-time data streaming
  • JSON-RPC: Inter-service communication
  • Model Context Protocol (MCP): Standardized tool/context access
    • OAuth integration
    • Security best practices
    • Tool exposure patterns
  • Agent-to-Agent (A2A): Multi-agent collaboration
    • Authentication and authorization
    • Verifiable audit trails
    • Cross-platform coordination

4. Memory & Context Management

  • LangMem for conversation memory
  • mem0 for persistent memory
  • Session management strategies
  • Context engineering techniques
  • RAG (Retrieval-Augmented Generation) integration

5. Agentic Design Patterns

  • Prompt Chaining: Sequential task decomposition
  • Routing: Dynamic agent selection
  • Parallelization: Concurrent agent execution
  • Orchestration vs. Choreography
  • Planning and Reflection
  • Tool Use Patterns

6. Cloud-Native Development

  • Containerization with Docker/Rancher Desktop
  • Kubernetes orchestration
  • Dapr distributed application runtime:
    • State management
    • Pub/Sub messaging
    • Workflows and virtual actors
    • Service invocation
    • Secrets management
  • Serverless containers (Azure Container Apps)

7. Production Deployment

  • CKAD (Certified Kubernetes Application Developer) skills
  • ArgoCD for GitOps
  • Observability and tracing
  • Cost optimization strategies
  • Security and compliance

8. Advanced Topics

  • Voice agents and conversational AI
  • Self-hosted LLM deployment
  • Fine-tuning large language models
  • Graph Query Languages for knowledge graphs
  • Enterprise authentication and authorization
  • Multi-modal AI systems

Learning Path Structure

Phase 1: Foundations (AI-101 - 4 weeks)

Prerequisite: Complete Modern AI Python Programming

Focus: Core Python for AI

  • Modern Python syntax and data structures
  • Type hints and static typing (MyPy)
  • Async/await programming
  • Object-oriented programming patterns
  • Pydantic for data validation

Repository: learn-modern-ai-python

Phase 2: AI-First Development (AI-201 - 14 weeks)

Weeks 1-2: Agentic AI & DACA Theory

Topics:

  • Understanding agentic AI vs. traditional AI
  • DACA design pattern principles
  • AI-first vs. cloud-first development
  • Hypothesis: Why agentic AI is the future

Learning Outcomes:

  • Understand the agent execution model
  • Grasp the challenge of planet-scale agent systems
  • Learn the DACA architectural approach

Weeks 3-7: OpenAI Agents SDK (5 weeks)

Topics:

  • Module 00: UV package manager & API setup
  • Module 01-05: Basic agents and tools
  • Module 06-10: Model settings, context, streaming
  • Module 11-16: Agent cloning, tracing, handoffs
  • Module 17-22: Structured outputs, guardrails, memory
  • Module 23-30: Custom runners, Chainlit UI, MCP integration

Resources:

Hands-On Projects:

  • Build a customer service agent
  • Create a multi-agent workflow
  • Implement tool integration patterns
  • Develop session memory systems

Weeks 8-9: Agentic Design Patterns

Topics:

  • Prompt chaining for task decomposition
  • Routing agents based on intent
  • Parallel execution strategies
  • Orchestration patterns
  • Planning and reflection loops

Resources:

Week 10: Memory Systems

Topics:

  • LangMem for conversation history
  • mem0 for persistent memory
  • Context window management
  • Memory optimization strategies

Week 11: Managed Databases

Topics:

  • PostgreSQL for structured data
  • Redis for caching and state
  • Cloud-managed database services
  • Data modeling for agents

Weeks 12-13: FastAPI Basics

Topics:

  • RESTful API design
  • Async request handling
  • Pydantic integration
  • API documentation with OpenAPI
  • Testing and validation

Week 14: Containerization

Topics:

  • Docker fundamentals
  • Dockerfile best practices
  • Rancher Desktop setup
  • Container networking
  • Hugging Face Docker Spaces deployment

Resources:

Evaluation: Hackathon 1 - 8-hour project using AI-201 stack

Phase 3: Cloud-First Development (AI-202 - 14 weeks)

Weeks 1-4: Kubernetes with Rancher Desktop

Topics:

  • Kubernetes architecture and components
  • Pods, Deployments, Services, ConfigMaps
  • Persistent volumes and storage
  • Networking and ingress
  • Local development with Rancher Desktop

Weeks 5-6: Advanced FastAPI with Kubernetes

Topics:

  • Microservices architecture
  • Health checks and liveness probes
  • Service mesh concepts
  • API gateway patterns
  • Load balancing strategies

Weeks 7-9: Dapr Integration

Topics:

  • Week 7: State management and Pub/Sub
  • Week 8: Service invocation and bindings
  • Week 9: Secrets and configuration management

Dapr Components:

  • State stores (Redis, PostgreSQL)
  • Pub/Sub brokers (RabbitMQ, Kafka)
  • Workflow orchestration
  • Virtual actors for stateful patterns

Weeks 10-11: Managed Services

Topics:

  • CockroachDB for distributed SQL
  • RabbitMQ for message queuing
  • Service selection and trade-offs
  • Cost optimization strategies

Weeks 12-13: Model Context Protocol (MCP)

Topics:

  • MCP specification and architecture
  • Tool exposure patterns
  • OAuth integration for security
  • Streamable HTTP transports
  • Building MCP servers and clients

Resources:

Weeks 14: Serverless Containers (Azure Container Apps)

Topics:

  • Azure Container Apps architecture
  • Autoscaling and cost optimization
  • Environment configuration
  • CI/CD integration
  • Monitoring and logging

Evaluation: Hackathon 2 - 8-hour agent-native startup project

Phase 4: Planet-Scale Development (AI-301 - 14 weeks)

Weeks 1-4: CKAD Certification Prep

Topics:

  • Core Kubernetes concepts
  • Pod design and deployment strategies
  • Configuration and secrets
  • Multi-container pods
  • Observability and troubleshooting
  • Services and networking

Goal: Pass the Certified Kubernetes Application Developer exam

Weeks 5-6: Agent-to-Agent (A2A) Protocol

Topics:

  • A2A protocol specification
  • Authentication and authorization
  • Inter-agent communication patterns
  • Trust and verification
  • Multi-agent orchestration

Resources:

Weeks 7-8: Voice Agents

Topics:

  • Speech-to-text integration
  • Text-to-speech synthesis
  • Real-time audio streaming
  • Conversational AI patterns
  • Multimodal agent design

Weeks 9-10: Dapr Agents & Google ADK

Topics:

  • Dapr Agents framework
  • Virtual actors for agent state
  • Event-driven workflows
  • Google Agent Development Kit (ADK)
  • Multi-agent hierarchies
  • Gemini and Vertex AI integration

Week 11: Self-Hosted LLMs

Topics:

  • Ollama for local LLM deployment
  • vLLM for optimized inference
  • GPU optimization strategies
  • Model quantization techniques
  • Cost vs. performance trade-offs

Resources:

Weeks 12-14: Fine-tuning LLMs

Topics:

  • Transfer learning fundamentals
  • LoRA and QLoRA techniques
  • Dataset preparation
  • Training and evaluation
  • Deployment of custom models
  • Domain-specific adaptation

Final Evaluation: CKAD + Dapr simulations + comprehensive project

Best Practices & Design Patterns

Security Best Practices

  1. OAuth Integration: Implement proper authentication for MCP servers
  2. Secret Management: Use Dapr secrets and Key Vault
  3. Input Validation: Leverage Pydantic models for type safety
  4. Least Privilege: Apply minimal permissions for agent actions
  5. Audit Logging: Track all agent decisions and tool calls

Reference: MCP Security Best Practices

Containerization Best Practices

  1. Multi-stage Builds: Minimize image size
  2. Layer Caching: Optimize build times
  3. Security Scanning: Regular vulnerability checks
  4. Non-root Users: Run containers with minimal privileges
  5. Health Checks: Implement proper liveness/readiness probes

Reference: Dockerfile Best Practices

Agentic Design Patterns

1. Prompt Chaining

  • Break complex tasks into sequential steps
  • Pass context between chain steps
  • Enable error handling at each stage
  • Use Case: Research agent → Analysis agent → Report generation

2. Routing

  • Classify user intent
  • Direct to specialized agents
  • Implement fallback strategies
  • Use Case: Customer service triage system

3. Parallelization

  • Execute independent tasks concurrently
  • Aggregate results efficiently
  • Handle partial failures gracefully
  • Use Case: Multi-source data gathering

4. Orchestration

  • Central coordinator manages workflow
  • Explicit control flow
  • Clear dependency management
  • Use Case: Complex multi-step business processes

5. Planning and Reflection

  • Agent plans before execution
  • Reviews results and adjusts
  • Iterative improvement loop
  • Use Case: Strategic decision-making systems

Framework Comparison

Framework Abstraction Level Learning Curve Control Simplicity Best For
OpenAI Agents SDK Minimal Low High High Most use cases, rapid development
CrewAI Moderate Low-Medium Medium Medium Role-based collaboration
AutoGen High Medium Medium Medium Conversational agents
Google ADK Moderate Medium Medium-High Medium Google Cloud ecosystem
LangGraph Low-Moderate Very High Very High Low Complex workflow control
Dapr Agents Moderate Medium Medium-High Medium Enterprise-scale stateful agents

Recommendation: Start with OpenAI Agents SDK for its simplicity and power balance, then explore others based on specific needs.

Additional Learning Resources

Top Agentic AI Repositories

1. LangChain & LangGraph Ecosystem

2. CrewAI Framework

3. Model Context Protocol (MCP)

4. RAG & Knowledge Systems

5. OpenAI Agents & SDKs

6. Agent Operations & Monitoring

7. Dapr & Cloud-Native

8. Learning Resources

Key Documentation Resources

  1. OpenAI Agents SDK
  2. OpenAI Cookbook - Prompting Guide
  3. Model Context Protocol Spec
  4. Dapr Documentation
  5. Kubernetes Documentation
  6. MyPy Type Checking
  7. Pydantic Documentation
  8. FastAPI Documentation

Video Resources

Certification Path

The Panaversity Certified Agentic and Robotic AI Engineer certification has 4 progressive levels:

Level 1: Beginner (Online, Proctored)

  1. Fundamentals of Modern AI Python L1

    • Core Python for AI (Colabs 01-09)
    • Data structures, control flow, functions

  2. Advanced Modern AI Python L1

    • Type hints and MyPy
    • Static typing concepts (Colabs 12-17)

  3. Fundamentals of Agentic AI L1

    • Intro to OpenAI Agents SDK
    • Basic Markdown
    • Agent concepts and workflows

Level 2: Professional (Manual Scheduling)

Required for faculty and product developers

  1. Fundamentals of Modern AI Python L2

    • 46 questions, 90 minutes
    • Advanced Python features
    • Generators, comprehensions, CPython quirks

  2. Advanced Modern AI Python L2

    • 50 questions, 2.5 hours
    • Static typing, asyncio, OOP
    • Pydantic v2, dataclasses

  3. Fundamentals of Agentic AI L2

    • 48 questions, 2 hours
    • Deep OpenAI Agents SDK knowledge
    • Async programming, multi-agent systems
    • Prompt engineering mastery

  4. Agentic AI Protocols L2

    • 100 questions, 2.5 hours
    • MCP and streamable HTTP
    • A2A communication protocols

  5. Agentic AI Memory, RAG & Design Patterns L2

    • Memory management
    • RAG techniques
    • Scalable AI architecture patterns

Level 3: Agent Native Cloud Professional

  • Docker containerization
  • Kubernetes orchestration
  • Dapr distributed runtime
  • Production deployment strategies

Level 4: Physical AI Professional

  • NVIDIA Isaac ROS
  • NVIDIA Isaac GR00T
  • NVIDIA Isaac Sim
  • Robotics and physical AI integration

Contact for Scheduling

  • Level 1: Online, scheduled exams (check Panaversity channels)
  • Level 2+: Contact Zia Khan via WhatsApp: +92-300-826-3374

Recommended Learning Journey

Month 1-2: Foundation Building

  • Complete AI-101 (Modern AI Python)
  • Study async programming and type hints
  • Practice with Colabs and hands-on exercises
  • Goal: Pass Level 1 certifications

Month 3-5: AI-First Development (AI-201)

  • Master OpenAI Agents SDK (Weeks 3-7)
  • Learn agentic design patterns
  • Build memory-enabled agents
  • Deploy to Hugging Face Spaces
  • Goal: Complete Hackathon 1

Month 6-9: Cloud-First Development (AI-202)

  • Kubernetes with Rancher Desktop
  • Dapr integration for distributed systems
  • MCP protocol implementation
  • Azure Container Apps deployment
  • Goal: Complete Hackathon 2, pass L2 certifications

Month 10-12: Planet-Scale Mastery (AI-301)

  • CKAD certification preparation
  • A2A protocol for multi-agent systems
  • Voice agents and multimodal AI
  • Self-hosted LLM deployment
  • Goal: Pass CKAD, build production-ready startup project

Month 13+: Specialization

  • Fine-tune LLMs for domain-specific tasks
  • Explore physical AI and robotics (Level 4)
  • Build and launch AI startup
  • Contribute to open-source projects

Key Success Factors

1. Hands-On Practice

  • Complete all Colab exercises
  • Build real projects, not just tutorials
  • Participate in monthly hackathons
  • Contribute to GitHub

2. Community Engagement

  • Join Panaversity community
  • Collaborate on group projects
  • Share learnings and get feedback
  • Mentor junior developers

3. Production Mindset

  • Focus on scalability from day one
  • Implement security best practices
  • Monitor and optimize costs
  • Think about observability and debugging

4. Continuous Learning

  • Stay updated with latest AI research
  • Explore new frameworks and tools
  • Read papers on multi-agent systems
  • Follow industry best practices

5. Entrepreneurial Focus

  • Identify real-world problems
  • Build MVP quickly
  • Iterate based on feedback
  • Plan for planet-scale from the start

Conclusion

This learning path provides a structured approach to mastering Agentic AI, from foundational concepts to planet-scale deployment. The DACA design pattern offers a proven framework for building resilient, scalable AI systems that can handle millions of concurrent agents.

Key Takeaways:

  1. Start Simple: OpenAI Agents SDK provides the best balance of power and simplicity
  2. Think Scale: Design for distributed systems from the beginning
  3. Embrace Standards: MCP and A2A protocols enable interoperability
  4. Practice Continuously: Hackathons and real projects solidify learning
  5. Build for Production: Cloud-native, secure, observable systems

Next Steps:

  1. Review the learning path and set your goals
  2. Start with AI-101 prerequisites if needed
  3. Enroll in AI-201 and begin with OpenAI Agents SDK
  4. Join the Panaversity community for support
  5. Build your first production-ready agent system

Additional Resources

Main Repository

🚀 Learn Agentic AI - Panaversity ⭐ 3.7K

Program Information

Design Patterns & Best Practices

Research powered by Octocode MCP ⭐🐙 https://github.com/bgauryy/octocode-mcp

Last Updated: November 9, 2025

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment