Hands-on • Code-first • Production-focused
Enterprise Agentic AI Systems
Build one production-grade AI platform, end to end. Across 15 modules, you'll engineer stateful agents, multi-agent orchestration, RAG, memory, and workflows - then make them enterprise-ready with evaluation, observability, governance, and cloud deployment. No isolated exercises: every module adds a real, integrated piece to a single system you ship, the way enterprise AI is actually built and operated.
Key Outcomes
- Design and build production-ready, stateful AI agents and multi-agent systems using LangGraph and modern agentic patterns.
- Implement enterprise-grade RAG, memory, and knowledge systems that ground AI responses in real organizational data.
- Apply cost engineering, failure engineering, and testing practices that make AI systems reliable and sustainable at scale.
- Build with security, governance, and observability, including access control, audit trails, and responsible AI practices.
- Deploy a fully integrated AI platform to the cloud using Docker, CI/CD, and infrastructure-as-code.
Curriculum Highlights
- 01 Enterprise AI Systems and Design Principles
- 02 LLMs, Reasoning, and Context Engineering
- 03 Building Agents as Stateful Systems
- 04 Agent Components and Runtime Architecture
- 05 Prompt Engineering and Structured Outputs
- 06 Tools, Skills, and Model Context Protocol (MCP)
- 07 Workflow Orchestration and Dynamic Execution
- 08 Intent Classification and Task Routing
- 09 Multi-Agent Collaboration Patterns
- 10 Knowledge Systems: Memory and Context Management
- 11 Enterprise Retrieval-Augmented Generation (RAG)
- 12 Model Adaptation Strategies
- 13 Evaluation, Reliability, and Observability Engineering
- 14 Security, Governance, and Responsible AI
- 15 Production Architecture and Cloud Deployment