Presented by Google Cloud
At first glance, data hurdles for today’s AI can feel like a replay of the siloed architectures that gave rise to big data analytics and customer 360 efforts. Indeed, bridging silos and making information accessible across fragmented environments have been core mandates for data leaders for decades.
But what's different this time is not just another silo problem on a larger scale. It’s that who’s acting on the data has changed. Previous modernization efforts unified data to be analyzed by humans. Agentic AI shifts that responsibility onto the data platform itself. When autonomous agents must act without a human in the loop, their outcomes become unreliable unless they’re anchored in operational context, business semantics, and trusted data.
To support this new pattern, enterprises must evolve the data layer from walled gardens that are passive storage into an active knowledge engine. Traditional storage and data catalogs exposed data for discovery, retrieval and human interpretation through applications. These catalogs evolved to become good at describing what data exists and where it lives. An active knowledge engine takes this a step further. It brings data, context, and business semantics to autonomous systems at runtime, grounding agent reasoning in the organization’s own definitions and operational reality.
The four requirements of an agent-ready data foundation
The first requirement of an AI data foundation is reach. Enterprise data is inherently distributed, spread across multiple clouds, legacy databases, on-premises servers, SaaS systems, and often spans structured and unstructured data (such as images, audio, and video). A “borderless lakehouse” approach tears down these walled gardens and unifies data into an open data estate for autonomous agents, eliminating redundant copies and complex data movement without forcing centralization.
The second requirement is context and trust, because data access is only the beginning. Once data becomes reachable, the platform must provide machine-readable context defining what the entities are, how they relate, and which business definitions apply. Humans can’t manually refresh this knowledge at agentic scale, so it must be supported by a unified trust fabric that goes beyond simple access controls. Provenance tracks where data originated, freshness checks verify it is current, and runtime attestation confirms that an agent produced a reported value using prescribed computations rather than an improvised process.
The third requirement is processing designed for machine scale. Routing large data volumes to large language models, rather than executing them at the data processing layer, can send token consumption and compute costs through the roof and create latencies. AI-native processing handles large-scale analysis efficiently, ensuring execution fits operational budgets and performance demands, instead of being an expensive, unbounded experiment.
The fourth requirement is agent tools and integrations. The data layer must expose its context and processing capabilities through agentic interfaces such as MCP and ADK so organizations can build domain-specific agents that act on trusted enterprise data. At the same time, AI agents are changing data work itself, boosting productivity in pipeline creation, data science, and model development while making data accessible through intuitive conversational interfaces. And because agents increasingly operate on the data layer, it must also capture their activity so that interactions and outcomes can become inputs for improving agent performance and efficacy over time.
Agentic AI changes the old modernization equation
Bridging silos and processing massive data in real time are familiar challenges. What’s new is requiring scale, immediacy, and data diversity simultaneously. Agents may need to combine transactional records with documents, images, conversations, and other unstructured sources, often locked inside SaaS applications behind APIs or spread across clouds with different governance models and egress costs. That requires real-time access across the relevant data estate without repeatedly copying information into another centralized system, along with infrastructure innovations that deliver the necessary price-performance.
Real-time access also carries greater consequences than it did during earlier analytics waves. A dashboard can present a delayed view for a person to consider. An agent operating inside a continuous loop must reason, act, observe the result and adjust while the operational context is still current.
Real deployments show how leading enterprises are evolving their data architectures to lay the foundation for exciting agentic use cases. For example, Yahoo uses a connected graph foundation at global scale to power real-time decision making across its agentic advertising platform. Its combination of operational graph capabilities with deeper analytical and predictive systems shows how live decisions and historical data combine in the same architecture.
This example demonstrates how unifying operational and analytical systems transforms the data platform into an active operating model for the automated enterprise.
A knowledge engine is not simply a better catalog
Wasn't the point of traditional data catalogs to aggregate metadata and help teams discover where assets live? Yes, metadata aggregation remains necessary, but describing location isn’t the destination. A true knowledge engine must go further to provide a reasoning layer that actively generates business definitions, infers relationships, and establishes what data means at the moment an agent acts.
The important distinction is active enrichment. An agent needs to understand the meaning of terms such as “booked revenue” versus “projected revenue,” grounded in the organization’s own definitions rather than a plausible interpretation generated from general model knowledge.
That enriched knowledge must then be available in real time to disambiguate requests from agents and avoid hallucination. Graph capabilities turn static metadata into a dynamic knowledge graph at runtime, allowing agents to traverse connected business concepts with well-defined paths and answer multistep questions, like why sales declined, or why a customer may leave. The goal is not simply better retrieval, but reasoning grounded in a common governed reality.
Demos and prototypes vs. production scale
Connecting an agent to a database through a direct Model Context Protocol or another interface can produce an impressive demonstration quickly. But a functioning connection doesn’t establish that the system can supply trustworthy context, operate across the enterprise estate, offer performance at scale, or remain affordable as workloads grow.
That’s the harder test of agent readiness. The meaningful divide is between organizations still working with isolated connections and those with a trusted data foundation powered by Borderless Lakehouse, Knowledge Engine, and AI-native processing that can enable truly autonomous agents to drive enterprise innovation.
Learn how to start scaling your AI initiatives with Google Cloud’s Agentic Data Cloud.
Neeraja Rentachintala is Senior Director of Product Management at Google Cloud.
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