Here’s what we kept hitting while building agentic security operators: every minute the AI spent figuring out which data lake the events lived in, which proprietary format the EDR exposed, which paywalled endpoint to call for the SIEM, was a minute it wasn’t doing the actual job.
An analyst navigates that mess because the analyst has years of memory and a UI to lean on. An AI agent has neither, and shouldn’t need them. Making the agent good at navigating tool sprawl is the wrong problem.
The right problem: AI needs a stack designed for it. Same SOC capabilities the human side has: telemetry, retention, detection, response, automation , but the categories the legacy stack carves them into (SIEM, SOAR, EDR, data lake) are business lines, drawn by vendors slicing the market for human buyers with separate budgets. An AI consumer doesn’t see those lines. So the AI-native stack collapses them: a single headless surface, uniform access, governable, data already shaped for an agent to reason over. Less weight to carry, fewer moving parts, far better economics than the legacy stack it sits alongside.
Then a second thing clicked. Building the agent once is the easy part; owning the outcome is the hard part: data sources drift, credentials start returning 401, the shape of the data changes, the goal moves. Someone has to notice and fix it. Today that someone is a human: a forward deployed engineer, a professional-services SOW, the AI-SOC vendor’s services team quietly doing the work behind the “autonomous” demo. If the way you get autonomous security is to ship humans in to wire it up and babysit it, it isn’t autonomous.
So we made that role AI. You describe the outcome, say, evaluate every quarantined-email restore request within five minutes and open a case if it’s wrong. An AI forward deployed engineer takes that as its charter, builds what’s needed to achieve it, and stays behind in the tenant: waking on its own schedule, checking the goal is still being met, fixing drift, pulling in a human only when it actually needs one.
We’re calling it Grid, and the private beta opens today: the AI-native stack, plus a forward deployed engineer that owns the outcome on it. The substrate is the same infrastructure LimaCharlie’s been running at production scale for years, redrawn for an AI consumer. You can’t vibe-code your way to that. Your legacy stack stays for the humans who use it.