From AI Demos to Defensible Decisions
AI agents rarely fail with a visible outage. They can return 200 responses while acting on stale evidence, retrying into duplicate side effects, using an unauthorized tool, or making a customer commitment the organization cannot later defend.
This hands-on session shows how to architect for that reality.
Rather than beginning with a model, prompt, or orchestration framework, participants start with the system’s promise: what is it allowed to attempt, what is it allowed to claim, and where does the platform stop trying and start owing?
Using a live AI-commerce scenario, we will design an agent that must answer a customer, evaluate a return, and potentially issue a refund. Then we will introduce realistic pressure: conflicting policy versions, stale inventory, ambiguous payment status, weak evidence, and a runaway tool-call loop.
Participants will use five practical artifacts from the companion lab: a Liability Boundary Map, Admissibility and Freshness Contract, Churn Budget, Stop Authority Ladder, and Decision Replay Card. The session closes by showing how the system can remain useful while safely deferring, freezing liability actions, or escalating to human review.
Attendees leave with a repeatable method for building AI systems that can act, stop, explain, and be trusted.
Learning outcomes:
- Identify the commitment boundary before designing components.
- Define evidence, authority, freshness, and policy required for AI action.
- Bound retries, fan-out, tool calls, latency, and cost.
- Design capability-scoped stop and re-enable controls.
- Create replayable proof for consequential AI decisions.
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