Architecting AI Enabled Microservices
Seven steps methodology for designing superior AI-Enabled Microservices
In most of the Monolithic applications business objects and data, model designs are already present. As part of moving to cloud, companies miss out of the use-cases the REST APIs need to support. Implementing cloud solutions on top of existing models may lead to performance issues with APIs and cause scalability issues. Clients need to rewrite code due to a new version of APIs.
In most of the Monolithic applications business objects and data, model designs are already present. As part of moving to cloud, companies miss out of the use-cases the REST APIs need to support. Implementing cloud solutions on top of existing models may lead to performance issues with APIs and cause scalability issues. Clients need to rewrite code due to a new version of APIs.
In this talk, we will explore ten steps methodology for designing superior Cloud Native RESTFul Microservices APIs. Firstly, define the business domain objects and how they relate to use cases. If the use-case is to support <500 ms response time and availability of 99.99%, design the application for Consistency, Availability, and Partition tolerant. Next, Create an ideal design which solves the use-cases, refer to the industry standard JSONs and designs from schema.org, iana.org, and microformats.org. Later, find the fail points in the process and go back to the first step to resolving the pain points: Go back to Define the problem. Question to ask is what can go wrong? When can it go wrong? Next, create a Facade pattern to connect to either the existing Monolithic App or create a new App to support the new cloud use-cases. Create API Gateway, so other companies can build software and create more offerings. Next, design common Layers for error handling, logs, and security. For API security, perform Threat Modeling to find security vulnerabilities and plan for mitigation of risks. Use generic authentication using SAML, OAuth, and JWT to support Authentication and Authorization. Next, create an API Versioning strategy so that the REST API can evolve with minimal client changes. Apply Cloud Native design patterns for Resiliency. In the end, test APIs using contract driven testing and PACT files.
This talk is ideal for the following roles:
Architects
Technical Leads
Programers
Integration Architects
Solution Architects
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