Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Monitoring AI agents in production is a fundamentally different problem from monitoring traditional software or even generative AI models. Because agents run autonomously, chain multi-step reasoning across tools and systems, and change behavior as their underlying models evolve, standard software metrics like uptime and CPU utilization miss almost everything that matters. ‍

Today, we’re excited to announce a few developer experience improvements to Cloudflare AI Search to make it easy to manage a search solution out of the box. Previously, you had to stitch together components of the Cloudflare primitives (Workers AI, AI Gateway, Vectorize, R2, Browser Run) but now, AI Search can do this automatically, and better. Our goal is to give your agents their own search engine, where they can easily find data to provide better answers for themselves and their humans.

Ask most security tools what an AI coding agent just built, and they can tell you. Ask what it was allowed to consume before it started, and far fewer have an answer. That gap, between watching agentic development and controlling it, is what securing it actually comes down to. Securing agentic development means stopping risk before it enters a build, not flagging it after. And risk prevention has a prerequisite most approaches skip: you can only account for the assets you actually hold and manage.

Agentic AI governance is about keeping powerful, autonomous AI systems aligned, safe, and accountable as they act on our behalf. It’s now a certainty that AI agents will be deployed enterprise-wide. So, we need to look more deeply into those agents, figure out where they are, how to find them, and fully understand what they are doing in deployment so we can prevent attacks. The most dangerous agentic attacks will not look like attacks at the layer where they originate.