Why Agentic AI Needs Federated Data Management at a Neutral Interconnection Hub

Adoption of AI agents will skyrocket in the next few years, and CIOs are still grappling with the implications of that. Gartner® predicts that “by 2028, an average global Fortune 500 enterprise will have over 150,000 agents in use, up from less than 15 in 2025, generating significant agent sprawl, IT complexity and management challenges.”[1]

Specifically, data management is becoming the defining challenge for enterprise AI. Unlike traditional AI applications, agents continuously reason, retrieve information, and act based on that information. To perform their best, these agents need consistent access to timely, contextual data.

Agentic AI doesn’t just increase demand for high-quality data; it fundamentally changes the data management equation. Enterprises need to rethink where they store their data and how they access it. Specifically, they need federated data management, where they move the query to the data instead of moving the data to the query. Compared to centralized data lakes, federated data management enables lower costs, more control over where data moves and how it’s shared, and better context for agents.

Data lakes still have a place, but not for real-time agentic workloads

Centralized data lakes remain a good fit for batch analytics and historical reporting, where stale data may be an acceptable tradeoff for simpler management and lower query latency. But agentic AI is different. Agents need the right data at the right moment, not the version that was last copied into a warehouse.

Every additional hop required to consolidate data into one place introduces staleness, cost, governance complexity and lost context along the way. Always-on, always-reasoning agents need a different approach.

Federated data management means moving queries to data, not data to queries

Federated data management flips the traditional model. Rather than copying data into a central lake, queries are executed where the data already lives. The system aggregates insights from each source and returns only those insights to the requester. The underlying data stays where it is. This means that the total data traffic is dramatically lower than that of a centralized approach. Also, agents get answers that reflect the current data, not a stale snapshot.

Rather than optimizing where data is stored, federated data management optimizes how quickly and cost-effectively agents can access trusted data where it already resides.

Hyperscalers, storage vendors and cloud native data warehouse providers have all begun offering federated data management capabilities: Google BigQuery now includes support for catalog federation,[2] while Databricks OpenSharing enables open, cross-platform data sharing.[3] These providers recognize that a cloud-first approach doesn’t always work, especially for data with special privacy and control requirements.

How it works: A four-layer architecture for federated data management

A federated data management strategy for agentic AI is built on four distinct layers:

• AI gateway: The “front door” for agents to access data. It has two lanes: an LLM gateway that routes calls to the right models based on cost, performance, quality, availability and compliance, and an MCP gateway that routes data and tool calls from the agent, enforcing authentication, rate limits, policy, and audit logging.
• Semantic layer: The layer that provides context for the data. It defines what the data means by applying business metrics, dimensions and logic; enforces row-level and column-level governance; and exposes discovery tools so that agents know what they’re allowed to ask for, instead of guessing at table and column names.
• Federated query engine: This layer has a reasoning engine that uses an AI model to decompose a query into sub-queries, translate it into the native language of each data source, push execution down to where the data lives, and aggregate the results. This layer keeps a metadata catalog that tracks attributes about the data sources (such as the physical location of the data).
• Distributed data sources: The files, object stores, vector databases, OLTP systems, and SaaS applications, wherever they reside. They stay in place; the query goes to them, and only the results come back.

Federated data management at Equinix

Why federation belongs in a cloud-neutral location

Data federation’s biggest weakness has always been the network. Every federated query crosses the network to reach each source. When it traverses the public internet between clouds, the latency and egress costs pile up quickly–the same problems federation was supposed to solve in the first place.

Placing the AI gateway, semantic layer, and federated query engine at a cloud-neutral interconnection hub removes that weakness. For instance, Equinix data centers sit within 1-2 milliseconds of most major cloud providers in many metros. Also, our Equinix Fabric® solution enables direct, private software-defined connectivity to hyperscalers, neoclouds and on-premises infrastructure, bypassing the public internet entirely. This means that:

• Cross-cloud and cross-source queries travel over private, low-latency links instead of the public internet.
• Egress fees are minimized, both because private interconnects have better rates than the public internet and because only the query results move, not full datasets.
• The architecture can reach every model provider and every data source from one physical location, without hair-pinning traffic in and out of any single cloud.

Advantages of hosting AI gateways at Equinix, compared to hosting in the cloud

In short, federation solves the logical problem of accessing distributed data without moving it. Equinix solves the physical problem of doing that quickly, cost-effectively, and securely, wherever the data and models happen to live. However, it’s not a one-size-fits-all solution: If all the applications and agents run within a single cloud provider, then it would make sense to deploy the AI gateway and semantic layer within that particular cloud provider.

Why data governance matters for agentic AI

Agents are tireless and unpredictable. They generate queries that no human analyst would, and many more of them. Without a single point of control, this unpredictability turns into risk: ungoverned access to sensitive systems, credential sprawl, and no consistent record of what agents have actually done.

This is why the MCP gateway matters. By routing every agent data request through one gateway, organizations get:

• One choke point to enforce policy. This ensures that row-level, column-level and access controls are applied consistently, and not configured separately in every source system.
• One audit trail to create a complete, centralized record of every query that every agent makes. This matters as much for compliance as it does for debugging a misbehaving agent.
• No credential sprawl, because the agent never holds real database credentials. It only talks to the gateway, which mediates access on its behalf.

As enterprises start to deploy thousands of agents each, this single choke point is what will keep data governance from becoming unmanageable.

The key ingredient in agentic data management: context

The federated query engine solves for “reach.” It gets agents to the data, wherever it lives. But reach doesn’t equal understanding. Agents can write queries that are technically valid, but completely wrong. They might sum the wrong revenue column, miss a filter for returned orders, or join on the wrong key. The queries will run fine, but the answers will be wrong.

This is the problem that the semantic layer solves. It sits between the agent and the raw data, translating business questions (“total revenue by region”) into governed, pre-defined logic that’s validated by a data analytics engineer. If an agent does request something invalid, the semantic layer returns a descriptive error that teaches the agent what’s actually available, so it can self-correct on the next attempt. The context layer also contains memory that keeps track of historical context data, helping provide better context for the queries sent to the model.

Without a semantic layer, federation would give agents wide-open access to raw data with no protection against using it incorrectly. With one, agents get the same reach, plus guardrails that keep their answers grounded in business meaning.

Federated data management in action

Customers across industries are taking advantage of Equinix’s vendor-neutrality to optimize federated data management for the agentic AI era. Let’s look at a couple of examples.

Large online retailer

This customer wanted to apply AI to accelerate partner on-boarding. They kept their vector database, semantic cache, and semantic layer at Equinix and then used our private interconnection solutions to access multiple clouds. This gave them flexibility to choose models from different providers, while also optimizing costs. For instance, they can run lower-cost open models on private AI infrastructure or on neoclouds, while running superintelligent models in hyperscale environments.

Global bank

This customer was running many agents and applications inside one cloud. For queries originating in that cloud, they deployed a dedicated AI gateway in that cloud to route queries to the appropriate models. For queries originating outside the cloud, they deployed a separate AI gateway at Equinix.

With multiple AI gateways working together in a federated manner, they’re able to optimize performance within their preferred cloud environment, while also maintaining flexibility to access models from different providers without unnecessary delays or costs.

Start building your agentic data pipeline today

In the agentic AI era, optimizing data management has become a source of competitive advantage. The enterprises that capitalize on this advantage will be able to connect their agents to the right data at the right time, optimizing cost-efficiency and avoiding risk.

Most enterprises are pursuing a distributed AI strategy today, where their data is spread across multiple clouds and private environments. This makes it impractical for them to centralize their data. Instead, federated data management helps them aggregate insights without moving the raw data. This empowers agents to respond faster to change, collaborate with peers, and improve the user experience.

As organizations build out their federated data strategy, deploying the AI gateway, semantic layer and federated query engine at a cloud-neutral interconnection hub helps them avoid the cost, risk, and performance penalties that come from constantly moving data to and from the cloud. Equinix colocation data centers are designed for exactly this purpose. Equinix offers global reach (for sovereign architecture), a vibrant partner ecosystem, and on-demand private connectivity to every cloud, model provider and data source that matters.

Learn more about how Equinix customers can optimize agentic AI by avoiding centralized data management and ensuring the free flow of data across different providers and environments. Read the brief “Optimize your global network for AI.”

[1] Gartner press release, Gartner Identifies Six Steps to Manage AI Agent Sprawl, April 28, 2026.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

[2] Neeraja Rentachintala and Tomas Talius, What’s new in BigQuery: Powering the Agentic Era, Google Cloud blog post, April 22, 2026.

[3] Databricks OpenSharing