The velocity paradox: Why AI governance breaks at machine speed

By Anand Madhav, Vice president – Analytics & AI, Straive

An agentic AI system recently processed 400 insurance claims overnight. The committee responsible for overseeing it meets once a week, on Thursdays.

That mismatch reveals something more fundamental than a scheduling problem. It exposes a governance model built for decisions that moved at human speed. Most oversight frameworks were designed around quarterly reviews, weekly stand-ups and after-the-fact audits. That cadence worked when AI systems were recommendation engines: a model proposed something, a person reviewed it, and the workflow had natural pause points for judgment to enter.

Agentic AI changes that model by removing those pause points altogether. These systems execute across applications, sequence their own decisions and act on them before anyone has opened a dashboard. A content moderation pipeline can flag and remove posts in milliseconds. A supply chain agent can reroute inventory based on demand signals. Execution has accelerated sharply, while oversight mechanisms remain tied to meetings and reviews. That gap is where risk builds.

This is not the first industry to confront governance at machine speed. Financial markets did so two decades ago. When trades moved across floors and phone lines, human oversight could keep pace: compliance officers reviewed orders, regulators spotted irregularities, and execution left room for intervention.

Electronic trading compressed that cycle to microseconds, and a single algorithm could fire thousands of orders before a compliance officer had finished the morning briefing. During the 2010 Flash Crash, the Dow fell nearly 1,000 points within minutes as automated systems interacted in patterns no oversight body had anticipated. The governance model built for a trading floor simply could not function at that velocity.

The industry did not solve this by hiring faster reviewers. It embedded governance into the system itself, through circuit breakers that halt trading when volatility spikes, pre-trade checks that reject orders outside defined parameters, and surveillance algorithms to catch patterns no person could spot in real time. Enterprise AI is now entering a similar phase, where governance must evolve alongside system speed rather than trail behind it.

Consider a typical agentic procurement workflow. It pulls vendor profiles from an approved supplier list, compares pricing across bids, checks each vendor’s compliance documentation, confirms budget availability and issues a purchase order, often within minutes. Risk accumulates quietly through systematic bias compounding across thousands of automated decisions, or workflows that technically follow every rule while drifting toward outcomes nobody intended. By the time a quarterly review catches it, the system may have been running that way for ninety days.

The instinctive response, and one we see in most organisations adopting agents, is to scale oversight the way one scales operations, by adding more reviewers and more approval checkpoints. This creates a different problem. Every human checkpoint throttles the system and erodes the value the automation was meant to deliver. An AI capable of handling a thousand cases a day gets capped at fifty because each one requires sign-off. Many organisations end up giving back, through added oversight, the very speed they invested in automation to gain. Governance running at human speed while systems run at machine speed does not make operations safer. It often just creates the appearance of control without the substance of it.

The alternative is governance-as-code, where controls are embedded into the system rather than layered afterward: boundary conditions the system cannot cross regardless of workflow logic, and monitoring that evaluates every decision in real time rather than a quarterly sample.

In the systems we have worked on, this looks like threshold-based approval limits that automatically route any purchase above a set amount to a human, role-based access boundaries that restrict what data an agent can touch, and real-time policy checks that validate each step.

Embedded controls only work if the system also knows when to stop. When a case falls outside defined parameters, it should stop, surface the decision to a person, and hand over enough context to act quickly. A person’s job here is the cases the system was never designed to handle.

These controls belong in the system’s design from day one. Access boundaries, decision limits, escalation triggers and decision logs should live in code, not policy documents reviewed once a year. Yet in many organisations, engineering builds the system first and governance reviews it later. By then, the decisions that determine how the system behaves have already been made. . In one deployment we reviewed, a procurement agent ran for four months before anyone in risk had seen its decision logic. Nothing had gone wrong. That was the unsettling part: nobody could have said so either way.

The organisations that solve this scale AI without constantly trading speed for control. It is worth asking, of any agentic workflow running today: if it made a bad decision at 2 a.m., how many hours would pass before anyone noticed?