Agentic Artificial Intelligence is transforming enterprise software by enabling autonomous systems to reason, plan, collaborate, and execute complex workflows with minimal human intervention. This course provides a comprehensive guide to designing production-grade AI agents capable of solving real-world business problems using modern Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, memory architectures, and workflow orchestration frameworks.
Students will build AI copilots, autonomous assistants, research agents, software engineering agents, customer support agents, and enterprise automation systems using LangChain, LangGraph, CrewAI, AutoGen, DSPy, MCP (Model Context Protocol), FastAPI, Docker, Kubernetes, Redis, PostgreSQL, and cloud-native deployment platforms.
The curriculum emphasizes reasoning strategies, tool calling, function execution, multi-agent collaboration, long-term memory, vector retrieval, observability, AI governance, and scalable production deployment.
Curriculum
- 9 Sections
- 0 Lessons
- 10 Weeks
- Module 1 — Foundations of Agentic Artificial IntelligenceDescription Understand the evolution of AI agents, autonomous reasoning systems, planning algorithms, cognitive architectures, and enterprise applications of agentic AI. Lessons Introduction to Agentic AI Intelligent Agents Autonomous Decision Making Agent Architecture Enterprise AI Automation AI Agent Lifecycle0
- Module 2 — Large Language Models for AI AgentsDescription Learn how foundation models enable intelligent reasoning, contextual understanding, planning, and autonomous execution for modern AI agents. Lessons LLM Fundamentals Context Windows Prompt Templates Function Calling Structured Output Reasoning Strategies0
- Module 3 — LangChain EngineeringDescription Develop modular AI applications using LangChain by implementing prompt templates, output parsers, retrieval pipelines, memory systems, and tool integration. Lessons LangChain Components Chains Tools Memory Output Parsers Agents0
- Module 4 — LangGraph Workflow OrchestrationDescription Design graph-based AI workflows using LangGraph to create deterministic, stateful, and scalable enterprise agent systems. Lessons Graph Fundamentals Nodes Edges State Management Conditional Routing Human-in-the-Loop0
- Module 5 — Multi-Agent CollaborationDescription Engineer collaborative AI ecosystems where multiple intelligent agents coordinate tasks, exchange information, delegate responsibilities, and solve complex business workflows. Lessons CrewAI AutoGen Multi-Agent Communication Task Delegation Agent Coordination Consensus Strategies0
- Module 6 — Memory Systems & Retrieval-Augmented GenerationDescription Implement short-term memory, long-term memory, semantic retrieval, vector embeddings, and Retrieval-Augmented Generation (RAG) to improve contextual reasoning and knowledge access. Lessons Memory Architectures Vector Embeddings Pinecone FAISS Qdrant RAG Integration0
- Module 7 — Tool Calling & Enterprise IntegrationsDescription Enable AI agents to interact with APIs, databases, cloud platforms, external services, and business applications through secure tool invocation and workflow automation. Lessons REST APIs Function Calling Database Integration MCP External Tools Workflow Automation0
- Module 8 — AI Agent Deployment & MLOpsDescription Deploy scalable AI agents using Docker, Kubernetes, FastAPI, Redis, observability platforms, CI/CD pipelines, and cloud-native AI infrastructure. Lessons FastAPI Docker Kubernetes Redis Monitoring Production Scaling0
- Module 9 — Enterprise AI Agent Projects0
Features
- LangChain & LangGraph Development
- MCP Integration Examples
- Architecture Diagrams & Templates
- Certificate of Completion
- Multi-Agent System Design Workshops
Target audiences
- LLM Engineers
- Machine Learning Engineers
- Backend Developers
- Enterprise Solution Architects
- Graduate Students in AI & Computer Science
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