AI-Era System Design & Architecture Mastery Katas
Classic system design teaches you how to scale requests. AI-era architecture teaches you how to scale reasoning, retrieval, tokens, tools, trust, and cost.
In the AI era, the best architects do not just draw boxes. They design authority, evidence, fallback, observability, and cost controls into every system.
Modern system design has entered a new era. It’s no longer enough to optimize for uptime and latency — today’s systems must also be AI-ready, token-efficient, trustworthy, and resilient. Whether building global-scale apps, powering recommendation engines, or integrating GenAI agents, architects need new skills and playbooks to design for scale, speed, and reliability.
This full-day workshop blends classic distributed systems knowledge with AI-native thinking. Through case studies, frameworks, and hands-on design sessions, you’ll learn to design systems that balance performance, cost, resilience, and truthfulness — and walk away with reusable templates you can apply to interviews and real-world architectures.
Learning Outcomes
By the end of this workshop, participants will be able to:
- Apply a 7-step AI-era system design framework to both interviews and real-world architecture reviews.
- Design systems that scale across requests, users, regions, tokens, retrieval calls, model latency, and inference cost.
- Architect production-grade RAG 2.0 pipelines using chunking, embeddings, hybrid search, reranking, GraphRAG, semantic caching, and freshness controls.
- Design agentic architectures with bounded tools, permission scopes, human escalation, audit trails, and safe degradation.
- Build multi-provider resilience strategies using model routing, fallback models, circuit breakers, budget caps, and graceful degradation ladders.
- Apply AI security patterns for prompt injection, indirect prompt injection, data exfiltration, unsafe tool use, and authorization bypass.
- Define AI observability and SLOs for hallucination rate, grounding quality, retrieval precision, drift, latency, token cost, and escalation rate.
- Practice architecture katas for e-commerce, ride-sharing, search, fraud detection, social feed, video streaming, and AI assistants.
- Defend trade-offs under interview pressure using clear diagrams, capacity estimates, failure-mode reasoning, and cost-aware decisions.
- Leave with reusable templates for AI System Design Canvas, RAG Checklist, Agent Safety Checklist, Token Capacity Planner, AI SLO Dashboard, and Chaos Runbook.
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of AI & Data Architecture at Salesforce, where he focuses on enterprise AI, agentic systems, cloud-native architecture, distributed systems, data platforms, security, and large-scale transformation.
Over his career, Rohit has designed and led complex enterprise platforms across AWS, Google Cloud, microservices, real-time data, API ecosystems, resilient distributed systems, and AI-enabled architectures. His work increasingly focuses on the challenges enterprises face as software evolves from deterministic services to AI-native and agentic systems—particularly around reliability, governance, evidence, security, observability, cost, and safe autonomy.
Rohit is the author of System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems, published by Apress. The book presents a modern approach to system design covering scalability, distributed systems, AI architecture primitives, security, reliability, economics, agentic systems, and real-world architectures including e-commerce, ride sharing, payments, fraud detection, messaging, video streaming, file storage, and search. (Springer Link)
Book:
Amazon: https://a.co/d/09Zs1twa
Publisher / Springer Nature: https://link.springer.com/book/10.1007/979-8-8688-2782-2
O'Reilly: https://learning.oreilly.com/library/view/system-design-with/9798868827822/
Rohit is also an O’Reilly instructor and a frequent speaker at technology conferences including No Fluff Just Stuff, UberConf, GIDS, and other international events. His talks focus on practical architecture lessons from building and operating complex systems, including AI control planes, trusted agents, inference at scale, evidence-first RAG, AI security, distributed-system failure, and AI-era software architecture.
As a trusted advisor and architecture leader, Rohit works at the intersection of business strategy and deep technical architecture—helping teams translate complex business problems into scalable, resilient, secure, and economically sustainable systems.
Rohit holds an MBA in Corporate Entrepreneurship from Babson College and graduate-level education in Computer Science from Boston University and Harvard University.
Connect with Rohit:
LinkedIn: http://linkedin.com/in/rohit-bhardwaj-cloud
X / Twitter: @rbhardwaj1