🚀 TECH 401 – ARCHITECT
Program Title: AI-First Architecture & Platform Design
Duration: 12 Sessions (2 Hours Each)
🎯 Program Objective
The objective of TECH 401 is to equip architects and technical leads with the ability to design enterprise-grade AI-first architectures, including multi-agent systems, orchestration layers, governance models, and reusable AI platforms.
Participants will learn how to embed AI into core architecture decisions, optimize cost and latency trade-offs, and define governance frameworks to ensure secure and scalable AI adoption across the organization.
Participants will learn how to embed AI into core architecture decisions, optimize cost and latency trade-offs, and define governance frameworks to ensure secure and scalable AI adoption across the organization.
✅ Pre-Requisites
- 6–10 years of engineering experience
- Demonstrated experience in system architecture or technical leadership
- Strong understanding of cloud-native systems and microservices
- Experience designing scalable, distributed systems
- Prior exposure to AI-enabled systems (recommended but not mandatory)
📘 Curriculum Overview
| Session | Topic | Detailed Coverage | Tools | Learning Outcome |
|---|---|---|---|---|
| 1 | AI-First Architecture Foundations | Design systems where AI sits at the core of workflows and application layers. | Miro, ChatGPT | Design AI-first systems. |
| 2 | AI Architecture Patterns | Explore orchestration layers, API-driven AI services, event-driven pipelines. | LangChain, Azure OpenAI | Apply suitable architecture patterns. |
| 3 | AI + Microservices Integration | Embed AI into distributed architectures while maintaining scalability. | Azure, OpenAI | Integrate AI into microservices. |
| 4 | Introduction to Agent Systems | Understand planners, executors & evaluators in agent workflows. | CrewAI, ChatGPT | Explain agent-based architectures. |
| 5 | Multi-Agent System Design | Design collaborative agent workflows solving complex tasks. | LangGraph | Build coordinated multi-agent systems. |
| 6 | Workflow Orchestration | Automate chained AI tasks across processing stages. | LangGraph, Azure | Orchestrate AI-driven workflows. |
| 7 | Cost Optimization Strategies | Token optimization, caching & usage monitoring. | Azure Cost Tools | Optimize AI system cost efficiency. |
| 8 | Latency Optimization | Batching, streaming & caching strategies. | Azure OpenAI | Reduce AI system latency. |
| 9 | Model Selection Strategy | Evaluate models based on accuracy, cost & latency. | OpenAI, Azure | Select optimal models for business needs. |
| 10 | AI Governance Frameworks | Responsible AI policies, compliance & governance structures. | Microsoft Copilot | Design enterprise governance frameworks. |
| 11 | Data Privacy & Security | Protect sensitive data & implement compliance controls. | Azure Security | Design secure AI systems. |
| 12 | AI Platform Design | Create reusable AI services, prompt libraries & APIs. | Azure, Copilot | Build scalable AI platforms. |
| 13 | Enterprise AI Adoption | Integrate AI across teams & organizational workflows. | Microsoft Copilot | Drive enterprise-wide AI adoption. |
| 14 | Case Study | Design enterprise-grade AI platform with agents & governance. | All Tools | Design complete AI-first architecture. |