Learning Paths for Technical Professionals

Agentic Architecture

This starter learning path provides a starter introduction to designing and engineering agentic AI systems. It covers agentic design patterns, multi-agent collaboration, LLM orchestration, frameworks like OpenAI SDK, Crew AI, LangGraph, and AutoGen, as well as tool integration, asynchronous workflows, persistent memory, and Model Context Protocol (MCP) for advanced tool and data connectivity.

Skills:

Agentic AI design patterns

Multi-agent system engineering

LLM orchestration and integration

Framework proficiency (OpenAI SDK, Crew AI, LangGraph, AutoGen)

Tool and API integration

Persistent memory and state management

Model Context Protocol (MCP) implementation

Autonomous agent deployment

AI system monitoring and visualization

Learning objectives:

  • Apply Agentic Design Patterns: Identify and implement core agentic AI design patterns such as Reflection, Tool Use, Planning (ReAct), and Multi-Agent Collaboration using frameworks like Ollama and OpenAI.
  • Engineer Multi-Agent Systems: Develop, orchestrate, and deploy collaborative AI agents using Crew AI, LangGraph, and AutoGen, integrating multiple LLMs and tools for robust workflows.
  • Integrate and Orchestrate LLMs: Build workflows that leverage multiple LLM APIs (OpenAI, Anthropic, Gemini, DeepSeek) and frameworks for advanced orchestration, evaluation, and feedback loops.
  • Implement Tool and Memory Integration: Connect AI agents to external tools, APIs, persistent memory, and databases using MCP, LangChain, and Gradio for enhanced autonomy and real-world applications.
  • Deploy and Monitor Agentic Applications: Create, deploy, and monitor autonomous AI agents and trading systems with error handling, visualization dashboards, and real-world tool integrations.

Target audience:

This path is designed for AI engineers, software developers, and technical professionals seeking to master agentic AI architectures. It is ideal for those with foundational programming knowledge who want to build, orchestrate, and deploy advanced AI agents and multi-agent systems in real-world scenarios.

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