Agentic AI and the Future of Enterprise Execution

Dev Nag, Founder & CEO ,QueryPal

Dev Nag is a technology entrepreneur and AI infrastructure leader with deep expertise in enterprise systems, machine learning and regulated operations. As Founder & CEO of QueryPal, he focuses on building execution infrastructure for AI agents operating within enterprise production environments. Previously, he founded Wavefront, which was acquired by VMware for $375 million. Nag also held engineering roles at PayPal and Google, and has published research in computational biology and AI, with multiple patents and widely cited technical contributions in machine learning.

The Growing Value of Enterprise Agentic AI

Enterprise-grade agentic AI platforms are valuable because they close the gap between what AI models are good at (reasoning) and what they should be reasoning about (all of the specific workflows that make your business run). Every company runs differently–fields mean different things, processes happen in different orders–and that organizational knowledge isn’t found in off-the-shelf AI; it has to be captured and integrated into models to generate real production ROI. The last few years of AI have been about figuring out how AI can work with enterprise declarative knowledge (documents, tickets); the next few years will be about optimizing AI around enterprise procedural knowledge (workflows and runbooks) to handle increasingly complex operational tasks.

The Balance Between AI Innovation and Enterprise Trust

The balance comes from making sure you have the environment to support both trust and innovation together, rather than seeing them as a trade-off. Most enterprises hit the wall with AI adoption and ROI because they bring in these platforms as standalone technologies without context, and fail to recognize how they work best. AI benefits from getting the deepest data context available, both declarative and procedural, and having a tight feedback loop to make sure it’s able to learn in a way that matches the business needs. When either of those pieces are missing, you see pilots fail to convert to production and future projects get scuttled.

Positioning Agentic AI Beyond the Hype

You communicate the real business value of agentic AI by showing measurable outcomes on real customer data against existing customer needs. AI doesn’t change the North Star of an organization; it changes how those goals can best be achieved. This can be demonstrated at various levels – from an automation flow that takes a routine task off a skilled worker’s plate to larger user metrics around satisfaction and resolution time. Some platforms shy away from measuring and showing business metrics without realizing that doing so disconnects the feedback loop that makes AI work so well in enterprises with the proper deployment philosophy. Nothing drives pilots into production faster than clear movement on metrics which have been stubborn for years inside an org.

  ​Nothing drives pilots into production faster than clear movement on metrics which have been stubborn for years inside an org.   

Separating Agentic AI Reality from Assumption

The most common misconception is that agentic AI is a magic bullet that can come in and change everything overnight. The highest-ROI projects are those which build in the affordances for change management—making sure data integrations are set up properly, that goals are clearly aligned, and that employees are equipped to work with the system—and not just focusing on narrow technical performance but the full ecosystem in which the technology will be embedded. 

The Future Impact of Agentic AI

Agentic AI is going to fundamentally change how enterprise work gets done – first by preserving and amplifying the declarative knowledge within a company, and then by getting at even more valuable procedural knowledge and making it available for other employees and to be automated. A common request we hear is enterprises wanting their level 1 support agents to perform like their level 3 support agents. We've now seen this happen in several different industries and verticals, and the pattern is always the same: once you capture how your best people actually handle the hard cases (the reasoning, the sequencing, the judgment calls) and encode it into a system that any agent or AI can invoke, the performance floor of the entire organization rises to match the ceiling.