Your AI agents are ready. Is your data?

Google Cloud introduced the Agentic Data Cloud at Google Cloud Next 2026, unifying data, AI models, and operational databases into a single System of Action to address infrastructure bottlenecks for agentic AI. A Google Cloud report found that 83% of organizations believe they require infrastructure upgrades to support production-grade agentic AI systems, while 43% cite difficulty integrating with legacy APIs and data sources as their biggest gap.

What’s one of the biggest bottlenecks stopping organizations from scaling their AI initiatives? It isn’t the capabilities of today’s models — it’s their access to business context and semantic meaning. In the agentic era, enterprises need to go beyond simply storing data to activating it with trusted context, moving from passive systems of record to proactive systems of action. But AI agents operate with nonlinear speed; for example, a single prompt can trigger the agent to independently browse, query, and execute across multiple systems, placing stress on the underlying infrastructure. If the compute, networking, and storage layers aren't optimized for agentic AI, the data platform sitting on top of them will buckle. It’s no wonder that, according to our State of infrastructure report https://cloud.google.com/resources/content/state-of-infrastructure-in-the-agentic-ai-era , 83% of organizations believe they require infrastructure upgrades to support production-grade agentic AI systems. To solve this problem, we introduced the Agentic Data Cloud at Google Cloud Next 2026; unifying your data, AI models, and operational databases into a single System of Action. To make an Agentic Data Cloud work, it must be AI-native from the chip to the model. The underlying infrastructure must be able to accommodate agentic load. Let’s explore how the right infrastructure foundation empowers an Agentic Data Cloud to solve the biggest data challenges organizations face today. To be effective, agentic systems require access to context that is often found in fragmented data systems and legacy architectures. This can make it hard for agents to get this context, leading to incomplete, inaccurate results. In fact, our report found that 43% of IT leaders cite “difficulty integrating with legacy APIs and data sources” as their biggest agentic AI infrastructure gap. But organizations cannot simply move massive datasets and connect them to AI without increasing complexity and cost. Our Agentic Data Cloud solves this by leveraging a borderless Lakehouse https://cloud.google.com/products/lakehouse?hl=en running on open, flexible infrastructure. By accessing powerful native engines like BigQuery and Spanner over open standards Apache Spark, Apache Iceberg , agents can read, reason over, and activate data across environments as if it were local, bypassing the latency and costs of traditional setups. Scaling agents on a patchwork of disconnected systems can create significant bottlenecks. In our research, 81% of leaders called out operational complexity and engineering overhead as top unforeseen expenses when scaling AI , citing the time engineers spend doing manual work to patch together AI agents across disparate systems. To move from thinking to doing, agents must be able to connect real-time data across both analytical and operational sources. This requires vertical integration. When an Agentic Data Cloud is built on an AI-native infrastructure where the models, data systems, and underlying accelerators are co-designed, there are fewer network hops and tooling is better integrated. This unified system allows an agent to reach an insight and trigger secure transactions without the typical engineering overhead. It’s not enough for agents to just discover and query data. To take safe, accurate actions, agents also need rich context and business logic. Yet, 36% of leaders cite a lack of specialized, high-throughput vector databases used for AI model grounding, as a key infrastructure gap, hindering their ability to give agents context. In order to work to their full potential, agents need a foundation which is built to read and write data systems in real-time, including legacy ERPs and third-party CRMs. It also gives them the long-term memory to recall a user’s preference from, say, three weeks ago, while executing a complex task today. And without this real-time automation, agents have to re-process data for every single query. To provide context for AI, organizations are using Knowledge Catalog https://cloud.google.com/blog/products/data-analytics/introducing-the-google-cloud-knowledge-catalog?e=48754805 to aggregate and enrich data in their data lakes, and enable agentic searches. By extracting meaning from unstructured data and automatically generating semantics, the catalog acts as an active reasoning layer. That catalog in turn, must be backed by high-throughput infrastructure, so that agents can retrieve the right context. To turn AI into a true competitive advantage, it’s time to build a connected, active data ecosystem. Giving your agents seamless access to all of your data is a must to move from pilots to production, and this must be supported by an infrastructure that can handle the demands of the agentic era. The winners in 2026 and beyond won’t necessarily be the ones with the smartest agents. They’ll be the ones who can feed those agents the right knowledge — securely, cost-effectively, and at scale. Is your data ready for the agentic era? See how leaders are taking an AI-optimized approach to architecture in the State of infrastructure in the agentic AI era https://cloud.google.com/resources/content/state-of-infrastructure-in-the-agentic-ai-era report.