Scope

Scope tackles an emerging problem that most product teams haven't even realized they have. As more users access B2B tools through AI agents, silent failures in agent workflows mean lost revenue without any visible signal in traditional analytics. Scope's simulation approach—testing how agents discover and use your product daily—exposes these failures before they compound. We'd reach for Scope when we've noticed that agent-driven conversions are flat or declining, and log analysis gives us no answers. The platform's strength is in pinpointing exactly where an agent stumbles: install errors, doc mismatches, broken API call structures. The built-in recommendations, like suggesting API endpoint changes or documentation fixes, turn raw telemetry into a prioritised fix list. Where it bites: Scope is in early access, so the integrations library is limited. Teams that don't yet route significant user traffic through AI agents may struggle to justify the investment. Also, implementing the recommended fixes still requires engineering bandwidth—Scope surfaces the issues but doesn't automate the remediation. Compared to traditional observability tools like Datadog or Sentry, Scope focuses on the agent's perspective rather than system metrics. It's closer in spirit to UX analytics at the agent level. That focus is its superpower, but also its risk: if the market shifts away from agent-mediated access, Scope's utility diminishes. In practice, Scope works best for B2B SaaS companies with an API-first architecture—those whose products are already tool-callable. If your product requires human visual interaction or isn't API-enabled, Scope can't help much. For the right team, though, it's an early-mover advantage in a space that's only going to grow.