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CloudaQube
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advancedAi MlPAID

AI Agents & Agentic Frameworks

A deep technical course on building production-grade AI agents. You will learn the theory behind agentic systems — the ReAct pattern, tool use, planning, and memory — then implement real agents using LangGraph, LangChain, and AutoGen. Topics include StateGraph construction with conditional routing and checkpointing, multi-agent orchestration with supervisor and handoff patterns, long-term memory with vector stores, and deploying observable, cost-controlled agents to production. Every concept is paired with runnable Python code drawn from the LangGraph, Anthropic, and AutoGen documentation.

12 hours
2 enrolled
Updated May 2026
Course Content ↓
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By CloudaQube Engineering

What You'll Learn

Explain the ReAct pattern and distinguish it from fixed pipelines
Implement tool use with the OpenAI and Anthropic function-calling specifications
Build stateful agents with LangGraph StateGraph including conditional edges and checkpointing
Integrate short-term and long-term memory into agent loops using vector stores
Design multi-agent systems using orchestrator, subagent, and supervisor patterns
Add human-in-the-loop interrupts and streaming output to LangGraph agents
Instrument agents with LangSmith, apply token budgets, and write agent behaviour tests

Prerequisites

  • Comfortable writing Python (functions, classes, async/await)
  • Familiarity with large language model APIs (OpenAI or Anthropic)
  • Basic understanding of prompting and chat completion formats

About the Instructor

C

CloudaQube Engineering

Expert instructor with hands-on industry experience in Ai Ml.

Included in paid plans

LevelAdvanced
Duration12 hours
Lessons—
Students2

This course includes

  • Hands-on practice labs
  • AI-powered explanations
  • Progress tracking
  • Certificate of completion
  • Lifetime access
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