Everyone Is Building AI Agents. But do the AI Agents understand your business?
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Our reflections from Cypher 2026
Ask an AI agent why sales dropped last month. Getting a generic fluent answer is easy. Getting an insight that uses the right revenue definition, connects inventory with customer demand, and respects access permissions is a challenge.
That challenge shaped what The Modern Data Company brought to Cypher 2026.
Under the theme “Building AI in India for the World,” the event put a spotlight on agentic AI and real-world applications. At Booth 23, we explored the foundation those ambitions depend on: giving AI the context to understand a business.
Making the context layer visible
“Business context” can sound abstract until you see it in action.
Through live DataOS® demonstrations, we walked visitors through the platform, its context layer, and ontology: the business entities and relationships that connect information with meaning. Customer stories grounded those capabilities in practical applications.
Take a customer, an order, and a delayed shipment. Each may sit in a different system. Understanding how they relate helps a team investigate the revenue impact and decide what to do next.
“An agent may know how to query a table. The harder question is whether it understands what that table means to the business. That is the gap enterprises need to close.” - Sanjoy Roy, VP-APAC & Middle East, The Modern Data Company
From demonstrating to documenting the craft
Cypher also marked the launch of a book on building data products, written by Dinker, TMDC’s Head of Solution Engineering, and his team Yogesh Khangode, Kishan Mahajan, Khushi Bedar, and Kamalika Sen. They as a group have created over 1,200 business data products across 25 business use cases.
Alongside the live demos, the launch brought another dimension to our presence: sharing the knowledge behind building. At any given time you could see visitors at the white board in the stall, discussing a problem statement and its solution.
As enterprises explore what AI can do, the discipline of making data understandable, reusable, and useful deserves equal attention.
Talking to data is the beginning
Our Talk to Data brochure introduced a compelling possibility: asking business questions in everyday language.
But a simple interface depends on substantial work underneath. Which source should supply the answer? What does “active customer” mean? Can everyone asking the same question see the same information?
Our white paper, Context for Enterprise AI: A DataOS Approach, explores how context can become consistent and reusable across an enterprise’s existing data environment.
We also shared 5 Emerging Trends in Enterprise Data & AI, whose interim survey findings highlight a pressing gap: AI agents are reaching production faster than enterprise data is becoming ready to support them.
The next question is the real test
For us at The Modern Data Company, these threads lead to the same priority: making enterprise AI dependable beyond its first impressive answer.
“The real test starts when someone asks the next question. Can the system follow the business relationships, respect permissions, and explain where the answer came from? That is what makes AI useful in everyday work.” - Dinker Charak, Head of Solutions Engineering, The Modern Data Company
Building AI in India for the world means making it work within the complexity businesses already have. With DataOS, our focus is on bringing context and governance across existing systems so enterprises can build from that foundation.
The question we left Cypher thinking about is simple: Your AI can access your data. Can it understand your business?
