A deep dive into the backend components of Enterprise AI applications, with hands-on implementation examples of Logs & Tracking, AI pipelines, Vectors & Databases, and 3 other key pillars that power high-scale Agentic AI services; the likes of which deployed at leading enterprises across the region.
The workshop will be structured around a series of blueprints and established patterns, following an inside-out approach to building backend systems that make agentic AI applications work. The workshop will be a mix of lecture-style content delivery, and hands-on implementation sessions where we will build out mini examples of the various components covered.

Course book included
Register early to receive a copy of the 67-page e-book on Agentic Patterns, revised and updated in February 2026.
This e-book is a great companion to the workshop, and provides great reference materials to early parts of the workshop.
...we have a toolkit of composable patterns that can inform the design of an Enterprise AI Agentic system. Such a design is only a blueprint — a conceptual architecture that needs to be materialized if they are to offer any business value in the real world. With this book, my goal is to provide a practical guide on materializing a given blueprint by grouping tasks-to-be-done into six functionally distinct pillars, which bear the acronym LEAVES 🌿.
Excerpt from The Enterprise AI Agentic Stack, Supertype Publication
Enterprise AI Blueprints
Recap of the key building blocks and implementation models from the 2-part Blueprint Series (Part 1: Agentic Patterns, Part 2: The Enterprise AI Agentic Stack).
- Building Blocks of Agentic AI Systems
- 6 Agentic AI Patterns expanded from Supertype's publication (updated Feb 2026)
- LEAVES: The front-end and back-end of enterprise AI implementation
Building an Enterprise AI Stack
Grouping AI implementation into front-end and back-end phases.
- Backend: The gut that powers AI applications (ingestion, retrieval, knowledge management).
- Front-end: Enterprise chatbots, AI Search, Report Generators, and more.
- 4 Enterprise AI Implementation Models from Supertype's publication
Engineering Practices and Enterprise Case Studies
An inside-out approach to building enterprise AI applications: LEAVES
- 1. The 4 Kinds of Pipelines for AI Applications
- 2. Memory-capable, Retrieval-Augmented, and Logging for AI-powered Chat and Enterprise Search
- 3. AI Server: Report Generators, LLM-powered APIs, and More
- 4. Knowledge Management: AI-powered Content Synthesis and Indexing with Vectors and Databases



