How Enterprises Are Re-Architecting Data For The Agentic AI Era

As enterprises transition to full-scale agentic workflows and automated decision engines, they are facing severe operational friction between rapid deployment and absolute data sovereignty. 

This challenge is becoming more pronounced because production-grade AI ecosystems require continuous, real-time data, leaving legacy governance models ill-equipped to handle fluid, unstructured conversational streams. 

As a result, for corporate boards and CXOs, the core challenge has shifted from extracting intelligence from data to scaling autonomous systems without losing control over their underlying data assets.

To decode these structural bottlenecks and map out a resilient data foundation for the agentic era, Inc42, in partnership with Skyflow, recently hosted an exclusive roundtable discussion titled ‘The Reality Of Scaling AI Without Compromising Data Control’ on the sidelines of Inc42’s AI Summit 2026.

Moderated by Sameer Dhanrajani, CEO of 3AI and AIQRATE, the closed-door session brought together a stellar cohort of tech architects, engineering heads and enterprise data custodians. 

Before we unpack the major themes that emerged from the discussion, here is the complete list of AI enthusiasts who joined the roundtable:

• Alok Dube, chief architect, Embitel Technologies
• Aman Bapna, VP of engineering and platform, Kuku FM
• Amruta Moktali, CPO, Skyflow
• Chitra Gurjar, VP of product engineering, Scripbox
• Kumar Pratik, head, engineering – monetisation, payments & logistics, Meesho
• Naveen Budda, cofounder, KarmaLifeAI
• Rajath Kedilaya, VP of product and technology, DrinkPrime
• Ramesh Kumar Saxena, head, data and AI platforms & engineering, PharmEasy
• Rohit Chatter, chief data and AI officer, AngelOne
• Sanjay Koppikar, cofounder and CPO, EvoluteIQ
• Satyajeet Singh, VP of product and head of AI, PayU
• Vinay Rai, executive VP of technology, Netradyne

The panel delved into three core architectural shifts: dismantling internal data fragmentation, the evolution of modern data engineering, and the rise of dynamic runtime security to comply with frameworks such as India’s Digital Personal Data Protection (DPDP) Act.

Deconstructing The Data Fragmentation Trap

The roundtable opened with an analysis of why data fragmentation — where information is trapped in isolated, disconnected departmental silos — remains a persistent executive hurdle despite decades of investment in centralised data warehouses and data lakes. 

The consensus among panellists was that these data silos are not a failure of technology but a natural reflection of how commercial enterprises are organised. Business divisions naturally create independent systems to optimise their individual workflows. However, this structural insulation creates immediate operational drag when speed becomes the primary metric for market growth. 

As AngelOne’s Rohit Chatter highlighted during the session, “When you define growth, you have to move at speed, and when speed comes in, centralisation takes a backseat.”

This layout complexity is further aggravated by a lack of shared definitions across business units; even if separate departments want to share information, they are often blocked because data parameters and metrics mean entirely different things across different software stacks.

Historically, enterprises lacked the clear financial incentives required to solve these deep definitional and structural issues. However, the commercial rise of machine learning and generative AI has completely altered the return-on-investment (ROI) calculations for clean data governance.

Corporate leaders are realising that because production-grade AI requires organised, contextual data to be effective, the financial value of fixing internal data plumbing has multiplied exponentially. Clean data is no longer a backend administrative chore; it is the core fuel for enterprise AI.

Shifting Towards Democratised Data Engineering

With clean data established as the foundational fuel, the role of data engineering is undergoing a massive transition. Traditionally, data engineering was treated as a rigid, backend process focused on data sanitisation. To resolve this fragmentation, modern data architecture must evolve alongside AI, redefining how business users interact with technical infrastructure.

Pointing to this shift, Meesho’s Kumar Pratik noted, “This entire data plumbing or tooling around data is less about data sanitisation, but it’s more about democratising data retrieval itself.” Today, this means making information instantly accessible and easily searchable for non-technical business users.

When building contemporary AI systems, engineering teams must prioritise this retrieval layer, synthesising both structured and unstructured data streams without creating systemic bottlenecks for employees. This requires looking at data through three simple layers: the data foundation at the bottom, the application at the top, and the orchestration layer in the middle that manages who is looking at the information and in what context.

This bottom-layer complexity becomes visible when independent software vendors (ISVs) and legacy enterprise systems lock data within proprietary schemas, preventing a singular source of truth from forming across the corporate landscape. To bypass this fragmentation without embarking on a multi-year database migration, modern data architecture is pivoting toward a data fabric approach. A data fabric acts as an intelligent, unified technology layer that virtually connects separate, scattered data systems, allowing files to stay exactly where they are while giving users and AI applications a secure, single view of information.

Ultimately, this push towards decentralised, user-centric retrieval layers changes how the entire enterprise data stack is organised. Industry leaders are discovering that best practices must focus on three fundamental blocks: helping users find data faster, helping them understand its context immediately, and establishing data trust. The goal is to focus less on over-engineering traditional backend databases and more on building agile, outcome-driven deployment models anchored to real-world business use cases.

Runtime Security And Agentic Privacy Playbook

However, anchoring data architecture to fluid, real-world use cases introduces a critical vulnerability: as teams build autonomous agents that continuously process information, traditional data protection paradigms completely collapse. Historically, corporate security was defined by broad, document-level access permissions — either an employee had permission to open a specific folder, or they didn’t.

The entry of unstructured data via generative AI means that security can no longer be defined by these permissions. Security must become incredibly granular and focus on individual content elements.

Highlighting this exact shift, Skyflow’s Amruta Moktali said, “With generative AI, unstructured data is entering the market. The tricky thing is that I want to protect elements inside a document. We need access levels on content inside the document, not just the document itself.”

This data orchestration challenge intensifies dramatically when a company scales across multiple distinct consumer applications, each with different monetisation journeys and content variations. If data engineering is not built correctly from the start, information coming from these different businesses is often force-fitted into a single, rigid structure, creating massive compliance risks.

To bypass this architectural rigidity, the panel agreed that data governance must move from static storage security to dynamic runtime protection. This means evaluating, validating, and filtering data in real time.

Implementing these active evaluation frameworks directly within transit layers allows organisations to dynamically enforce role-based data visibility. If a user queries a system using natural conversational phrasing rather than rigid code, the system must recognise who the user is and instantly redact sensitive information at the element level in real time. 

The cohort emphasised that comprehensive, real-time runtime auditing remains the only definitive way to scale enterprise AI ecosystems and satisfy evolving regulatory frameworks, such as India’s landmark Digital Personal Data Protection (DPDP) Act.

Rather than trying to force all corporate data into a single, centralised repository, the future blueprint relies on creating decentralised knowledge graphs and context layers. Data can stay safely wherever it currently lives, while a dedicated, intelligent orchestration layer manages verification, transit security, and architectural accountability.

Securing Transit Layers: The New Blueprint For Agentic Workflows

The insights uncovered during this closed-door roundtable point to a profound architectural shift. In an era where data is constantly in motion across unstructured pipelines, corporate security can no longer remain static. Protecting the enterprise now requires a blueprint in which security is unified, granular, and executed in real time, without introducing processing latency that compromises the user experience.

Ultimately, the transition to production-grade agentic AI is forcing a complete re-evaluation of the enterprise data stack. To scale safely, modern organisations can no longer rely on the blunt instruments of the past, whether that means forced data centralisation or rigid, document-level folder permissions.

By systematically dismantling internal information silos, modernising data engineering for democratised retrieval, and securing data at the element level in transit, enterprises can achieve the ultimate operational balance. Resolving this tension allows corporate boards and CXOs to confidently unlock the hyper-velocity growth of autonomous AI, all while maintaining absolute sovereignty over their most valuable digital assets.

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