If you map the tools your organization uses to manage AI agent risk, you will find good coverage at the edges. Governance platforms document policies. Security scanners test code before it ships. Monitoring tools log activity after the fact. What the map reveals – if you draw it honestly – is a gap at the center: at the moment an agent actually takes an action. That moment is the one that matters most, and it Read more
Most enterprise problems arrive one at a time. The control gap in AI agent governance is unusual because it creates three distinct problems simultaneously: a security exposure, a compliance liability, and a drag on the very adoption it is supposed to be enabling. All three share the same root cause: there is no deterministic control point between your AI agents and the consequences of their actions. The security exposure AI agents take actions. They write Read more
Something unusual is happening in enterprise technology right now. Organizations are deploying AI agents at a speed that their governance, risk, and compliance functions have not matched. This is simply the nature of how fast the underlying technology has matured and how compelling the productivity case has been. But the gap between deployment velocity and governance readiness has become large enough that it is now a category risk, not just an operational concern. The scale Read more
AI agents are useful because they act. They write code, run commands, query databases, and make changes to systems – often faster than any human reviewer can follow. That speed is the whole point. But it creates a problem most organizations are only beginning to confront: if an agent does something it should not have done, when did it happen, why did no one stop it, and where is the evidence you were exercising any Read more
Your AI agents are already running – and so is the meter. Every prompt, every tool call, every retry costs real money. For most enterprises that have moved beyond single-agent experiments, that spend is growing fast. Some of it is unavoidable. But a meaningful slice – the part that comes from agents repeating work they already did, reading files they already read, and producing verbose output that no one asked for – is pure waste. Read more
