The chatbot era is over. 2026 is shaping up as the year of the AI agent. Recent announcements from companies like Oracle and Databricks make that clear. These companies are no longer purely focused on conversational AI. They are rolling out systems designed to act autonomously, booking travel, executing trades, deleting files, and managing supply chains, without human intervention.
On paper, it looks like the productivity breakthrough companies have been chasing for years. In practice, it raises a more foundational question: what happens when autonomy scales faster than governance?
To understand why this moment is different, imagine hiring an employee who operates at machine speed, has access to sensitive financial systems, never sleeps, and cannot clearly explain their decisions. That is starting to become the reality of today's agentic AI. Most of these systems rely on probabilistic models, meaning they operate by calculating likelihoods, rather than following deterministic rules. When an AI agent chooses to liquidate a stock position or deny a loan application, "it made a best guess" is not an answer regulators, auditors, or courts are likely to accept.
"We're moving from AI that advises to AI that acts," said Rob Feldman, CLO of EnterpriseDB (EDB). "If an organization lacks strong governance and control disciplines, it will struggle in this environment. Agentic AI doesn't create new responsibility problems so much as expose existing ones."
Security analysts argue that agentic AI cannot be safely deployed without something akin to a "flight recorder." That means an immutable audit trail that captures what an agent accessed, what actions it took, and what systems or data sources shaped its decisions.
In other words, governance cannot live only inside the model itself.
Most AI systems remain probabilistic and opaque by nature. They don't provide the deterministic traceability that regulators, auditors, or courts expect when something goes wrong. That's why many analysts argue these logs must be stored in a strict, transactional system of record designed for accountability, auditing, and post-incident review.
"Models may be opaque, but governance can't be. The organizations that succeed are the ones that treat control, auditability, and accountability as foundational, not optional," said Feldman. The urgency behind these concerns was underscored by a recent CISA warning issued about "data poisoning." Attackers are no longer focused solely on stealing information. Increasingly, they are trying to subtly alter data in ways that mislead AI agents into making the wrong decisions.
Without a strict, cryptographically secure database that logs every change, organizations may have no indication they were compromised until financial losses or operational failures surface.
The rapid push to deploy autonomous agents is fueling excitement across the tech sector, but it is also racing ahead of the safety frameworks designed to keep those systems in check. If 2026 is going to be the year of the AI agent, it also needs to be the year of the audit.
In response, companies like EDB are repositioning their platforms beyond traditional data storage, framing them instead as sovereign AI control planes; environments where data and AI execution are secured, governed, and continuously monitored. In a world increasingly run by autonomous software, the most valuable asset may not be intelligence itself, but control over it.
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