Overcoming the biggest blocker to AI production

Autonomous AI agents are already running inside core infrastructure – executing code, applying policies, and managing DevOps functions.

And the projects keep stalling, because the security models they’re being wired into were built for a world that no longer exists.

Retrofitting non-deterministic actors into those models is costing engineers time they don’t have, and it introduces risk no enterprise can manage.

Many projects have stalled amid concerns about deploying without a robust security foundation – and with good reason.

We’ve already seen an agent delete a company’s entire production database, and its backups, in nine seconds.

Security teams are being asked to stop scenarios like that with tools built for a world of two actors.

The cracks are starting to show.

Something has to change, or innovation stalls under the weight of its own controls.

AI agents require a new identity model The pressure on production and engineering teams to speed up delivery is very real and pervasive.

So they often fall back on old habits like granting agents broad privileges and treating them as any other microservice.

But agents are very different from machines; they are error-prone and non-deterministic, just like humans.

Yet, operating at machine speed, 24/7.

Agents can delete entire production databases in nine seconds.

How many humans do you know who could do that? And this brings me to the crux of the problem.