Every organization these days wants to take advantage of AI, but few know how to deploy it. Even fewer know how to make it work safely at scale.
As a result, there’s a growing gap between impressive demos and systems that won’t hallucinate, break compliance rules, or produce bad outputs. And for most organizations, that gap remains unbridged.
Saptarshi Banerjee is building the infrastructure that might just close this gap. He specializes in agentic AI, focusing on making generative AI functional and useful in the real world, complete with better context and operational safeguards.
From Hackathons to High-Stakes Systems
While pursuing a graduate degree from Illinois Tech in Chicago, Banerjee started participating in just about every hackathon he could attend, including events at UC Berkeley, the University of Michigan, and the University of Illinois Urbana-Champaign. These were high-stakes, sleepless sprints where teams had 36 hours to build in front of industry judges.
At HackIllinois, for example, Banerjee’s team developed an early prototype of an on-demand healthcare platform that could connect patients with nearby providers. It was basically Uber for healthcare, but years before these apps became commonplace.
Taking the $6,000 grand prize was a huge win for Banerjee and his team, but his takeaway was something much more meaningful: “This project was a moment of clarity,” he says. “It showed me that tech could solve critical problems before the market even recognized them. It sparked a passion for building scalable, AI-driven platforms. Not just functional ones, but transformative ones.”
It’s a passion that he still has to this day. Only now, the stakes are considerably higher than a weekend competition.
Turning AI Pilots into Production Systems
Today, Banerjee builds AI systems for organizations that are graduating from pilots to full-fledged deployments. These aren’t just startups or solopreneurs… many are established enterprises that have to comply with strict regulatory demands.
This means that on any given week, Banerjee might be building healthcare AI that can pull patient records from multiple databases, check treatment plans, compare against clinical guidelines, flag drug interactions, and support compliance with requirements such as HIPAA. The next week, he’s working on a financial AI deployment that can review transactions or documents while considering money laundering rules and fraud controls.
But what’s stopping these organizations from just pulling up ChatGPT to do this? Well, it’s not quite as simple as using a public LLM. Besides the disastrous security implications of using a consumer-grade tool to work with sensitive data, the problem is that these platforms are trained on very specific data. They don’t have the context needed to answer an organization’s most important questions.
That’s why Banerjee relies on advanced frameworks like retrieval-augmented generation, agentic AI workflows, and advanced orchestration patterns to ground artificial intelligence in relevant, up-to-date data. He specializes in providing that kind of valuable context and helping AI better understand a company’s goals, react to changing conditions, and stay within defined boundaries.
In doing so, he’s refined the art of creating AI agents that excel in planning, retrieving information, and completing tasks.
A Legacy of Knowledge and Open Innovation
Outside of his career, Banerjee has become a pillar in the industry as a thought leader and expert in agentic AI. He’s written two books that are currently under contract with Springer Nature, including Agentic AI Systems in Practice, a book that focuses on designing autonomous agents that harmoniously coincide with human oversight. The second is Scaling and Integrating Generative AI Across Enterprise Systems, a book that tackles the problem of AI’s integration with legacy systems.
“These books distill years of practical insights, architecture patterns, and research-backed strategies to help organizations and practitioners operationalize responsible AI at scale,” Banerjee notes. They’re written, he says, for developers, architects, and engineering leaders who are in the trenches, working day in and day out to make AI more dependable.
His other writings include “Evaluation of State-of-the-art Deep Learning Techniques for Plant Disease and Pest Detection,” a paper that was published in Computers, Materials & Continua. This paper focused on the use of deep learning image analysis models for diagnosing plant diseases, all with the goal of improving crop health and food security.
Banerjee has also presented at major technology and policy events, including conferences like AWS re:Invent and AWS Summits, and at the SCSP + AI Summit, discussing how AI will affect the world moving forward.
He also regularly contributes to open-source projects like the AWS Serverless Patterns Workshop, a collection of reference architectures that have received over 4,000 GitHub stars and are used by developers worldwide to speed up cloud-native adoption. They also encourage modular deployment and operational efficiency, which are principles that are often missing from flashy demos but still essential for enterprise-grade success.
Finally, Banerjee has served as a judge and mentor at AI competitions like the QS Reimagine Education Awards and hackathons backed by organizations like Google DeepMind and Y Combinator. He’s also organized one of the Bay Area’s largest upcoming AI hackathons, which offered more than $50,000 in prizes.
“The best AI breakthroughs aren’t locked in labs,” he says. “They’re built in the open, shaped by the community, and judged in hackathons where the only limits are time and imagination.”
Where Agentic AI Goes Next
In recognition of his contributions to the industry and to the next generation of AI specialists, Banerjee was made a Senior Member of IEEE in 2025 and profiled in Outlook India for his work on responsible AI and cloud technologies.
His overarching goal, he says, is to “become a global voice and thought leader in artificial intelligence. Someone who not only builds cutting-edge systems but also shapes how AI is adopted, governed, and scaled responsibly across industries.”
If there’s one takeaway from his work, it’s that agentic AI does not have to remain theoretical. With the right structure (clear context, guardrails, and accountability), it can function as a trusted part of enterprise systems today, not just as a promising demo on a conference stage.