AI Architecture
A 12-week deep dive into the architecture patterns behind production AI systems. Multi-agent orchestration, model serving infrastructure, and the platform engineering that makes AI products reliable at scale.
12
WEEKS
48+
HOURS
12
PROJECTS
$499
PER YEAR
User Request
"Analyze this codebase"
ORCHESTRATOR
plan
route
decide
merge
LangGraph state machine
Research
active
Coder
active
Reviewer
idle
Deploy
waiting
MCP TOOLS
GitHub
Postgres
Slack
Search
FS
MODEL POOL
Claude
GPT-4o
Llama 3
Gemini
LAYER 1
LAYER 2
LAYER 3
LAYER 4
What You'll Learn
12 weeks of deep, practical content. Each week builds on the last.
AI Product Architecture Patterns
- Monolithic vs microservice patterns for AI systems.
- When to use agents vs chains vs simple prompts.
- Latency budgets for AI-powered features.
- Architecture decision records for AI trade-offs.
LLM Serving Infrastructure
- vLLM for high-throughput inference.
- Ollama for local development workflows.
- Modal and serverless GPU for burst workloads.
- OpenAI vs self-host vs hybrid — the cost decision framework.
Multi-Agent Orchestration
- LangGraph for stateful multi-step agent workflows.
- CrewAI for role-based agent collaboration.
- Google ADK for agent development.
- Supervisor patterns and task decomposition strategies.
Agent Communication Patterns
- Inter-agent messaging and state sharing.
- Human-in-the-loop checkpoints and approval flows.
- Error recovery and fallback chains.
- Agent memory: short-term, long-term, and shared.
Structured Output Architecture
- Instructor for reliable data extraction.
- Pydantic AI for type-safe agent interactions.
- DSPy for programming (not prompting) models.
- Output validation pipelines and schema enforcement.
Model Gateway Design
- LiteLLM for universal model access.
- Model routing based on task complexity and cost.
- Rate limiting, auth, and usage metering.
- Streaming SSE and WebSocket patterns for real-time AI.
Live Class Schedule
Every Tuesday
Architecture Reviews
8:30 PM IST | 8 AM PST
11 AM EST | 5 PM CET
Every Thursday
AMA Sessions
8:30 PM IST | 8 AM PST
11 AM EST | 5 PM CET
Every Saturday
Live Classes
8:30 PM IST | 8 AM PST
11 AM EST | 5 PM CET
Not for Beginners. Not Sorry.
This track assumes you can code, you've shipped to production, and you're ready to level up.
Platform Engineers
You build internal platforms and now need to add AI capabilities. This track teaches you to design AI platforms that your product teams can actually use.
AI/ML Engineers Going to Production
You can train models and build prototypes. This track teaches you the production architecture that makes AI reliable, observable, and cost-effective.
Engineering Managers Leading AI Teams
You need to make architecture decisions for AI products. This track gives you the technical depth to evaluate proposals and set direction.
ENROLL NOW
$499/year
Annual payment. Lifetime access to content and updates. No subscriptions ever.
Enroll Now →Or view all pricing plans including all-tracks access
Common Questions
How is this different from AI Native System Design?
AI Native focuses on data architecture (embeddings, RAG, vector stores). AI Architecture focuses on compute and orchestration (agents, model serving, platforms). Many students take both.
Do I need to know Kubernetes?
Basic container knowledge helps but isn't required. We focus on architecture patterns, not infrastructure ops. You'll understand what to deploy, and your DevOps team can handle how.
Will I build multi-agent systems?
Yes. By week 4 you'll have built agent systems with LangGraph, CrewAI, and Google ADK. By week 12 you'll have designed a complete production AI platform.
Ready to level up?
12 weeks. Real projects. Senior engineers only. The next cohort starts soon.
Enroll Now — $499/yr →