Agent demand can’t be forecast, meaning the database has to be ready for anything
That has practical consequences for platform design. Enterprises will need to think about concurrency controls, caching strategy, semantic and vector retrieval paths, federated access to multiple data stores, and whether real-time agent queries should run directly on operational data or through a dedicated analytical serving layer. A database that is fast in benchmark conditions may still fail under agent-driven fan-out, where one prompt turns into many downstream lookups across structured and unstructured sources.
The governance angle is equally significant. “Guardrails” for agents are not just a policy feature; they require enforceable data-domain boundaries, fine-grained authorization, lineage visibility and observability that links an agent action back to source queries and underlying datasets. In regulated or air-gapped environments, that operational traceability may matter as much as speed. Before adopting an agent-ready database, teams should test for three things: burst-concurrency behavior, blast-radius containment when agents over-query, and the quality of audit data available when an agent retrieves or infers something it should not.
Enterprises building an agentic AI architecture are learning that the database matters as much as the model. AI agents fire off thousands of unplanned queries at once — and expect answers in milliseconds.
PhoenixAI, the trade name of CelerData Inc., raised $80 million this year to develop database technology for AI agents to solve this growing problem. Established players struggle to return answers in real time, according to Ross Armstrong (pictured), senior vice president of global sales of PhoenixAI.
“There was a real gap in the market for real-time analytics, and to be able to not only ask a question but get the response back in real time was something that all the database players that are out there, even the big names in the market, couldn’t do well,” Armstrong said. “If you have customer-facing analytics or agentic AI … you need that response time to be able to meet the SLAs with your customers.”
Armstrong spoke with theCUBE’s Paul Gillin at Cloudera EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the database demands of agentic AI, real-time query performance and the need for governance and observability. (* Disclosure below.)
Building an agentic AI architecture for unpredictable demand
Agents generate queries in real time and may hit several databases at once, according to Armstrong. PhoenixAI’s specific columnar engine handles structured and unstructured data and works out how to answer each query as it arrives.
“I think what the biggest demand is that we don’t know what the demand is going to be from those agents,” Armstrong said. “You could have … thousands and thousands of queries hitting at the exact same time, but you still need that real speedy response.”
PhoenixAI joined Cloudera as a foundational database partner and is now an option in Cloudera’s marketplace. Its self-managed edition, Original Posthoenixai-anywhere" shape="rect">PhoenixAI Anywhere, runs in a customer’s cloud, on-premises or in air-gapped environments. In a market where many early adopters hit governance and observability problems, PhoenixAI plans to address them with AgentBase, a purpose-built context environment that keeps agents away from data they don’t need, Armstrong explained.
“You can design that playground for those agents to be protected and to be governed but also to give them access to the data that you need,” Armstrong said. “It’s not necessary to run agents on a solution like PhoenixAI, but it’s certainly one that helps you put those guardrails in that a lot of organizations are looking for.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Cloudera EVOLVE26:
(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Agent demand can’t be forecast, meaning the database has to be ready for anything
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