Percona CEO says ideal database for AI agents doesn’t exist yet
The Percona CEO says there is no single database that currently fits all AI agent workloads, highlighting that the field is still evolving and that researchers and vendors are debating what features are most needed. This explainer summarizes what was discussed, who could be affected, and practical steps to consider as AI agents become more common in organizations.
What was said and why it matters
Peter Farkas, CEO of Percona, described at a recent Percona Live event in Amsterdam that the ideal database for autonomous AI agents does not exist yet. He suggested that workloads driven by AI agents are iterative and may require capabilities not found in today’s databases. The broader point is that the AI workload category is still evolving, and a future database design may be needed to handle agented tasks efficiently.
Key facts about AI agent workloads and databases
- AI agent workloads refer to automated software that makes decisions, queries data, and acts on results with minimal human input.
- Current databases often specialize in traditional transaction processing, analytics, or mixed workloads; none are universally optimized for all AI agent patterns.
- Experts suggest a clean-slate approach could be required to fully meet these needs, but practical requirements are still debated.
Who could be affected by this shift
Organizations building AI assistants, autonomous services, or automated decision systems may face choices about how to store, access, and update data fast enough for AI agents. IT teams, data engineers, and product developers will need to consider new data architectures, vendor tools, and potential standards as the field evolves.
Practical next steps for organizations
- Assess current AI workloads: identify where latency, data consistency, and scale are critical for agent decisions.
- Experiment with mixed environments: combine databases optimized for transactions with those focused on analytics and vector similarity search to support different AI needs.
- Monitor standards and vendor plans: stay informed about evolving requirements for agent-based workloads and any proposed database design directions.
- Plan for data governance and security: ensure data used by AI agents complies with regulations and internal policies, especially with automated decisions.
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