AI agents have moved from research demos into daily endpoint workflows. Developers install code-writing assistants with local file access; analysts trial autonomous tools that fetch data, run scripts, and call APIs. Each agent introduces new permission surfaces—filesystem, network, OAuth scopes, API tokens, package managers—and traditional EDR views these as just another process. The result is shadow agents proliferating faster than security teams can inventory them. The core problem is not only malware; it is over-privileged, unvetted automations that execute legitimate actions with unintended consequences, from exfiltrating customer data to poisoning repositories via compromised dependencies.
Glow’s bet is to make AI agents first-class citizens in endpoint security. Instead of waiting for indicators of compromise, it continuously maps installed and ephemeral agents, associates them with capabilities (tools, plugins, model calls), and evaluates contextual risk: what the agent can touch, which credentials it holds, how it sources packages, and whether controls like EDR are degraded. Policies then enforce pre-admission guardrails—blocking a risky npm install, isolating an agent invoking disallowed tools, or requiring step-up approval for high-impact actions. This is prevention aligned to agent intent and permissions, not generic process telemetry alone.
For buyers, the question becomes where this layer sits in the stack and how to operate it without throttling developer velocity. The pragmatic approach is to integrate with MDM for deployment, EDR for signal fusion, and IdP/SSO for identity-aware policies, while offering developer-friendly exceptions and auditability. Success metrics should include reductions in shadow agents, prevented risky package pulls, closure of EDR coverage gaps, and mean time to policy remediation. Watch for data handling practices, on-device versus cloud inference, and a policy-as-code model that enables versioning, dry-runs, and staged rollouts across tens of thousands of devices.