At TechSparks 2026, one of the more pointed arguments from the startup and venture ecosystem came not from a model builder, but from the infrastructure layer. Nitin Waghulde, Regional Director for APAC at VeloDB, said the industry's enthusiasm for AI agents has outpaced the systems needed to make them reliable in production.
The pattern, he suggested, is now familiar. A team builds a pilot, the agent performs well in a controlled environment, and the demo wins attention. But once the same system is exposed to real users, live data, latency constraints and unpredictable workflows, performance degrades. The failure is rarely about the headline-grabbing model alone. More often, it is the stack underneath that cannot sustain the demands of persistent memory, retrieval, concurrency and operational consistency.
Demo To Deployment Gap
Waghulde's remarks speak to a broader problem now confronting enterprise AI adoption in India and globally. Many companies have moved quickly from experimentation to proof-of-concept deployments, encouraged by the promise that agents can automate customer support, internal search, workflow orchestration and decision assistance. Yet the distance between a polished pilot and a dependable production system remains wide.
In practice, AI agents are not just language models that generate text. They are systems that must remember prior interactions, fetch relevant context, call tools, update state and respond in near real time. That means the database is no longer a passive store of records. It becomes part of the reasoning loop. If it is slow, fragmented or poorly designed for vector and transactional workloads, the agent's output can become inconsistent, stale or simply unusable.
Waghulde's argument lands at a moment when investors and founders are reassessing where the durable value in AI will accrue. The first wave of excitement focused heavily on models and interfaces. The next phase is increasingly about infrastructure that can support production-grade workloads at scale. For database companies, that shift is strategic. For startups building agents, it is existential.
Why The Stack Matters
The core challenge, as Waghulde framed it, is that production AI requires more than inference. Agents need context windows that extend beyond a single prompt, retrieval systems that can surface the right information quickly, and storage layers that can handle both structured and unstructured data without breaking under load. They also need governance, observability and fault tolerance, especially when deployed in customer-facing or business-critical environments.
This is where many pilots fail. A prototype may rely on curated data, limited users and manual oversight. Production introduces noisy inputs, edge cases and scale. If the database cannot support hybrid search, low-latency access and reliable state management, the agent may hallucinate, repeat itself, lose context or make poor tool calls. The result is a system that looks intelligent in a demo but behaves brittlely in the wild.
For venture investors, that distinction matters. Startups that present themselves as AI-native are increasingly being judged not only on model performance, but on whether they have solved the unglamorous infrastructure problems that determine retention and enterprise adoption. In that sense, Waghulde's comments reflect a market correction: the winners may be those that treat data architecture as a core product advantage rather than an afterthought.
Infrastructure Becomes The Moat
The message from TechSparks 2026 was not that AI agents lack commercial promise. It was that their promise depends on the reliability of the systems around them. As enterprises move from experimentation to deployment, the database layer is emerging as a critical moat. It determines whether an agent can remember, retrieve, adapt and scale without collapsing under operational pressure.
That is especially relevant in India's startup market, where companies are racing to embed AI into customer service, fintech operations, commerce workflows and internal productivity tools. The pressure to ship quickly can encourage teams to optimize for the demo rather than the deployment. Waghulde's warning is that this approach is unsustainable. The real test begins after the pilot ends.
For VeloDB, the pitch is clear: if AI agents are going to become dependable enterprise systems, the database must be built for the agentic era, not retrofitted for it. In a market crowded with model announcements and product launches, that is a more technical, less glamorous argument. But it may also be the one that matters most when the demo lights go off and the production workload begins.