Know exactly what your agent did, before the support ticket does
A user says the AI did something weird. The real trace should take minutes, not a support escalation.
Prefactor is an agent evaluation platform for teams building AI agents: it observes every agent run from development to production, evaluates whether the agent did its job, and shows how to improve it.
Works across OpenAI, Claude, LangChain, CrewAI, and MCP-connected workflows: the same trace, whatever you built on.
flagged: exceeds max_discount policy (15%)
How do I know whether a change to my AI agent made it worse?
Compare every run before and after the change. Prefactor evaluates every run, so when a model is updated or a prompt is edited, drift shows up as a trend rather than as a user complaint. You can roll a change out to a small share of traffic first and check the scores before shipping to everyone. When a user reports something odd, the trace and the verdict for that run are already recorded.
Small changes, and then one day the agent isn't doing what it used to
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.
Behavior changes without a deploy
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.
"What actually happened?" has no fast answer
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.
Dashboards show it happened. They don't stop it
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.
What changes before your next rollout
Every interaction, queryable
Runtime activity, connected systems, and risky actions are tracked as they happen, so investigating a report means searching a record instead of guessing.
Restrict before you roll out broadly
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.
A regression shows up as a trend
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.
Ship to 5% before you ship to everyone
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.
Confidence, not a hope
Watch how a feature behaves as workflows, integrations, and operational scope evolve, before deciding it's ready for everyone.
Iterate on what actually happened
Runtime trend data across a canary group tells a team whether a change is working, not just whether it shipped.
Expand on evidence, not a deadline
The decision to go from 5% to 100% is backed by the same trace that would explain an incident, not a calendar date.
The problems this solves
Frequently asked questions
How does Prefactor help product teams investigate AI issues?
Can Prefactor help us ship AI features more safely?
How does Prefactor catch operational drift?
Does this work across the frameworks we already use?
Related glossary terms
See your own agent's trace, not a demo one
Book a demo and we'll walk through investigating a real incident, on a fleet like yours.
Unified performance platform for agents, authentication, and risk management