Madhuri Somara explains why production agentic AI depends on the systems around the model: evaluation, observability, human oversight, failure handling, and trust.
About Madhuri Somara
Madhuri Somara is a Product Leader at Microsoft focused on AI agents, agentic platforms, and enterprise AI. She leads product strategy and execution for AI-powered customer service experiences, helping transform complex business processes into intelligent, action-oriented workflows. Her work spans customer intent, case enrichment, follow-up, resolution, observability, evaluation, and responsible deployment of autonomous AI at enterprise scale. Madhuri is passionate about building AI that helps people rather than simply replacing them, with a particular focus on human oversight, transparency, measurable outcomes, and trust. She has spoken at major technology forums including Microsoft Ignite and AI-focused industry conferences and serves as a mentor, advisor, and judge across technology and startup communities. Her perspective centers on one fundamental question: how do we move AI from impressive demonstrations to systems that organizations can confidently trust in production?
What motivated you to join the AI Builders Global Conference?
Absolutely. What excites me most about AI Builders is the opportunity to connect with people who are not just talking about AI, but are actively building what comes next. We are at an important transition point, from AI that assists people to AI that can understand intent, make decisions, take action, and operate within real business workflows. I wanted to be part of a community exploring that transition honestly: what works, what breaks, and what it really takes to move agentic AI from an impressive demo into something people can trust in production.
What shaped your journey through technology and AI?
My journey has been centered on building technology that solves real problems for people and businesses. As a Product Leader at Microsoft, I work at the intersection of AI, enterprise applications, and customer service, with a focus on AI agents and agentic platforms. What drew me deeper into AI was seeing the difference between technology that simply generates an answer and technology that can actually help someone complete a job. That distinction became very real to me as I worked on AI agents that can interpret customer intent, enrich cases, follow up, and help drive cases toward resolution. When you see a system take something that previously required a person to read, interpret, decide, and act, and turn it into an intelligent workflow, the potential becomes very tangible. My focus today is not simply on making AI more autonomous. It is on making it usefully autonomous, observable, controllable, and trustworthy.
What does it take to put agentic AI into production?
Getting an agent to work in a demo is very different from getting an agent to work reliably in the real world. Production environments are messy. Customers have different processes. Data can be incomplete. Instructions can be ambiguous. Systems fail. Connectors behave unexpectedly. When an AI agent takes an action, there needs to be a way to understand why it acted, what happened, and what a human can do when something goes wrong. I believe the next phase of agentic AI will be defined less by how impressive an agent looks and more by whether organizations can confidently put it into the hands of their employees and customers. That means building the right guardrails, evaluation, observability, human oversight, failure handling, and measurement around the agent, rather than treating those as afterthoughts. For me, the real question is no longer, "Can AI do this?" It is "Can we trust AI to do this at scale?" That is the shift I'm most excited about.
Who should attend the AI Builders Global Conference?
I would recommend it to anyone who is trying to understand where AI is actually going, not just where the headlines say it is going. That includes product leaders, engineers, AI builders, founders, researchers, enterprise technology leaders, and anyone responsible for bringing AI into real organizations. If you are building an AI agent, considering your first production deployment, trying to understand how autonomous systems should work with humans, or simply trying to separate the promise of agentic AI from the practical realities of deploying it, this is a valuable community to be part of. The most interesting conversations in AI right now are happening at that intersection between possibility and production.