ASAPP’s Priya Sawant on the New Rules of Platform Engineering

Engineering teams building with generative AI are facing a challenge: How do you move faster while making sure customers can trust your technology? Recently, Solidigm’s Jeniece Wnorowski and I sat down with Priya Sawant, senior vice president of engineering at ASAPP, to talk through that tension. ASAPP builds the chat and voice AI agents that power contact centers for large enterprises, and Priya’s teams sit at the center of a shift redefining how engineers work and what they build. ‍

Speed Without Sacrificing Trust

Priya described the change as twofold: Her engineers now write code with a different set of tools, and the products they ship look different, too. Generative AI is probabilistic by nature, she said, and you have to make sure that your customers believe that your technology is doing exactly what you’re saying it’s doing. Building evaluation outputs and visibility into agent behavior became a priority for her teams. Speed matters at ASAPP, she said, but not at the cost of the hygiene that earned the company its customers in the first place.

Golden Pathways’ to Adoption

Priya said her platform teams design tools that make engineers’ jobs easier, creating “golden pathways that everyone else can follow.”

She breaks the discipline into three phases:

‍Design: Teams interview internal users to understand their problems and craft solutions to address them. ‍
Build: Teams pull out the backlog item most needed now. ‍
Adoption: A surge of early interest settles into steady, lasting use.

A platform built without attention to developer experience creates friction, Priya said, and friction kills adoption.

Look for the Common Denominator

Priya said that when addressing internal engineering problems, you have to identify “the common denominator of the challenges that you want to solve for” and provide flexibility on top of that. Security and reliability, for example, stay fixed, while other components are more pliable. She pointed out that bottom-up adoption, spread through engineer-champions who find real value early and convey that enthusiasm to their peers, works better than top-down mandates. ASAPP borrows a page from open-source communities here, running user groups where engineers weigh in on different tools and the build plan.

The Expanding Surface Area

Platform teams still do what Priya calls “glue work”: stitching disparate systems into a coherent experience for internal customers, and AI tools are giving these teams the same productivity boost that product engineers already enjoy. But with this boost comes more complexity: The surface area that platform teams need to cover has grown fast, spanning new models, inference platforms, frameworks and experimentation tools that shift by the week. ASAPP’s own tooling shows that shift in miniature. Engineers used to build language-based frameworks that baked in the company’s best practices, making it easier to spin up a new service. Now the team builds a base agent framework so that anyone plugging a new agent into ASAPP’s platform starts from a shared foundation rather than building one from scratch. That same instinct shows up in the product itself, as an observability suite letting enterprise customers verify that their deployed agents hold to business policy, paired with what Priya calls “the agent flywheel”: a system that mines usage data to surface the next automation opportunity. Human-in-the-loop design rounds out the approach, keeping a person in the workflow wherever regulation or customer preference calls for one.

The TechArena Take

Priya’s conversation offers a lesson for engineering leaders navigating the AI transition. Successful teams will build evaluation, observability and developer experience into the product at the start instead of patching them on after something breaks. Underneath all of it sits a storage and compute layer that has to keep pace with what these agents demand. ASAPP’s golden-pathway approach to platform engineering, paired with attention to what internal engineers need, offers a working model for enterprises that want to move fast on AI without losing the trust that took years to build.

To hear the full conversation, listen to the podcast episode or visit asapp.com.