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Mitul Tiwari

Co-founder & CTO, Stealth

Mitul Tiwari is CTO and Co-founder of a stealth AI startup. Until recently he was a Director of AI and Machine Learning Engineering at ServiceNow leading natural language technologies group. Earlier he was CTO and Co-founder of Passage AI (acquired by Servicenow). His expertise lies in building data-driven products using AI, Machine Learning and big data technologies. Previously he was head of People You May Know and Growth Relevance at LinkedIn, where he led technical innovations in large-scale social recommender systems. Prior to that, he worked at Kosmix (now Walmart Labs) on web-scale text categorization, and its applications. He earned his PhD in Computer Science from the University of Texas at Austin and his undergraduate degree from the Indian Institute of Technology, Bombay. He has also co-authored more than twenty publications in top conferences such as ACL, AAAI, KDD, WWW, RecSys, VLDB, SIGIR, CIKM, and SPAA.

Mitul Tiwari

Sessions / 2025 / 1 talk

  • TapeAgents: A Powerful Framework For Building And Optimizing AI Agents

    TapeAgents: Advanced Framework for Observable AI Development | Discover ServiceNow's open-source framework for building transparent, debuggable AI agents with comprehensive action recording and replay capabilities. Learn how TapeAgents' innovative recording system enables unprecedented visibility into agent behavior, streamlined debugging, and data-driven optimization. Master practical techniques for building robust AI agents with built-in observability and performance analysis tools. Features implementation strategies for creating production-ready agents with enhanced reliability and maintainability.

Sessions / 2020 / 1 talk

  • Lessons Developing Conversational AI Virtual Agents

    This talk will cover building conversational AI using deep learning technologies and lessons learnt in developing conversational interfaces. The first part of the talk will describe recent advances in deep learning that has led to tremendous progress in natural language processing and is making conversational AI a reality. Conversational AI includes intent classification, sequence labeling, understanding dialogs and context, and coming up with responses to users messages. The second part of the talk will address lessons learned developing conversational virtual agents. A conversational virtual needs to be personable in addressing, adaptive in understanding, and available to automate different supported tasks. Overall, key take aways from the talk will be better understanding of (1) deep learning techniques for natural language processing and (2) interaction patterns for automation using a conversational virtual agents.

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