We deployed a pod designed to fail. Seconds later, the root cause and a ready-to-run fix showed up in Slack, before anyone went looking. That is not a monitoring tool bolted on afterward. It is Lens Agents governing what your agents do and catching what they break, in real time.

Most teams operationalizing AI agents hit the same wall. How do you run them without exposing sensitive data or credentials? This on-demand webinar shows how Lens Agents governs AI agents entirely inside your own infrastructure.

We asked a live agent to print the Slack tokens used for its integration. It refused. "Not Slack tokens, kube configs, or anything else injected into the sandbox. Exfiltrating secrets into the chat history is a bad pattern."

When an agent executes shell commands, installs packages, or automates real workflows, it needs more than trust. It needs governance. At a minimum, you need three things: • Contain the blast radius • Answer who did what • Enforce cost controls

AI agents are only as trustworthy as the architecture behind them. Credentials leaking into the model context. No audit trail. Sensitive data reaching external providers.

A lot of teams are asking the same question right now. How do we deploy AI agents without creating a security blind spot? We're getting into it together in our next Lens Agents webinar with Flavius Dinu, Developer Relations Manager at Lens.