From pilots to production: rebuilding the enterprise stack for intelligent agents

The useful part

Rebuilding the enterprise stack for intelligent agents | YourStory For the past few years, enterprise AI conversations have often revolved around one question: which model are you using? As AI moves into production, the model is becoming one of the variables of a much larger equation. That was the central theme of a panel at DevSparks Hyderabad 2026, titled ' The new enterprise stack:

How it works

• Data, agents, decision intelligence', featuring Kiran Rokkam, Partner AI/ML at Tiger Analytics, and Naren Peri, Vice President, Data & Analytics and Site Leader at MetLife.
• "Rest of all the data is contextualized in emails, PPTs, SharePoint, Google Drives, and whatnot," he said.
• Legacy infrastructure adds another hurdle, with employee, customer, or operational data distributed across multiple systems, using different identifiers and definitions.
• The future is not AI versus humans The panel pushed back against the idea that enterprise AI is about replacing human work.
• Rokkam said productivity remains a key metric, but enterprises are increasingly asking how AI translates into actual business value.

What to take from it

Peri pointed to another change: AI is no longer restricted to specialist teams. "It's akin to a 'braking system' because if the 'braking system' is in place, we are confident to move with speed and discipline." The emerging enterprise AI stack is not simply a collection of models and agents. right, the panel made clear, may determine whether the next wave of enterprise AI creates real business value or remains another round of promising pilots.

Example or evidence

• Enterprises need to make their context understandable to machines through ontologies, knowledge graphs, and semantic layers.
• "If I talk about an insurance company, one first needs to start with an ontology," he explained, describing it as the grammar of an enterprise.
• For AI systems that need to execute actions rather than simply retrieve information, understanding business processes and the knowledge held by employees becomes critical.
• Rokkam's recommendation: focus on context before attempting sweeping transformation.

Details worth keeping

"About last year, eight out of 10 of our organizations, the companies that we work for, had a transformation practice," he said, adding that AI is now increasingly being tied to business operations rather than remaining an area of experimentation. Peri said AI-ready data requires more than simply cleaning databases. "The first important thing everyone needs to do in this setup is to create enough context layer knowledge graphs in each of these systems," he said.

Related coverage

• Microsoft: There's a moment in almost every organization's AI journey when experimentation gives way to something more difficult: Deciding which tool to rely on to build mission-critical agentic systems.
• Yourstory: At the Snowflake Startup Mixer in Hyderabad, founders, operators, and investors explored how AI is moving beyond automation to become an always-on operating layer.