A user says the AI did something weird. Prefactor gives you the real trace in minutes: which tool ran, what triggered it, and whether it's a one-off or a pattern, instead of a log-scrolling exercise that ends in a shrug.
Works across OpenAI, Claude, LangChain, CrewAI, and MCP-connected workflows: the same trace, whatever you built on.
Models get updated, prompts get tweaked. Each edit looks fine alone, until a user hits a case nobody tested and the first anyone hears is a support ticket.
A prompt edit, a model version bump, an integration change: any one of them can shift what an agent does, and nothing announces it happened.
When a user reports a problem, the team often lacks visibility into what the agent accessed, what it decided, and why, so investigating means reconstructing a story from scraps.
Traditional observability tooling surfaces telemetry after the fact. It can't hold a risky action before it reaches a user, or flag that this week's behavior has quietly moved.
Runtime activity, connected systems, and risky actions are tracked as they happen, so investigating a report means searching a record instead of guessing.
Action-level restrictions, blocking, and approval routing mean a new feature ships with a ceiling on what can go wrong, not just a hope that it won't.
Changes in prompts, permissions, and behavior are tracked against an agent's own baseline, so a shift surfaces before it becomes a pattern of user complaints.
A monitoring dashboard tells you an agent misbehaved after enough users noticed. Prefactor's runtime boundaries mean a risky action can be blocked or held for approval before it reaches the next user, and the trace that explains why is already there when someone asks. Observability and intervention aren't the same job, and shipping fast needs both.
A new AI feature doesn't need every user on day one. Roll out to a slice, watch how it actually behaves, and expand on evidence instead of hope.
Watch how a feature behaves as workflows, integrations, and operational scope evolve, before deciding it's ready for everyone.
Runtime trend data across a canary group tells a team whether a change is working, not just whether it shipped.
The decision to go from 5% to 100% is backed by the same trace that would explain an incident, not a calendar date.
Book a demo and we'll walk through investigating a real incident, on a fleet like yours.
Prefactor helps teams observe, evaluate, and improve their AI agents in production — across every framework and provider.