Real failure modes from real agent fleets, and how each one gets caught.
Agents fail quietly: wrong outputs, silent errors, agents nobody registered. How teams find hidden failures in production AI agents, and how Prefactor surfaces every run in one record.
What it looks like, and the fix →02Agent output is checked by hand, experts become the QA bottleneck, and accuracy plateaus below target. How teams define what good looks like and evaluate every agent run automatically.
What it looks like, and the fix →03The agent pilot proved value in weeks, then spent months waiting on risk and compliance sign-off. Why approval stalls, what evidence reviewers actually need, and how a per-run record shortens the path to production.
What it looks like, and the fix →04AI agents repeat what they read: customer records in prompts, PII in outputs, company data in unapproved tools. How teams record what every run touched, check scope on each one, and hold a breach before output leaves.
What it looks like, and the fix →05AI agent bills reach five figures a month with nothing tying spend to output. How teams attribute cost per agent, per version, per task, and pause a run before the overrun.
What it looks like, and the fix →06Dashboards report agent failures after the fact. How teams check every run against an activity schema as it happens, hold or block a breaching action before it completes, and stop one agent without shutting down the fleet.
What it looks like, and the fix →07Agent hallucinations look like competence: fluent, specific, and wrong. Why they happen, how a groundedness verdict on every run catches them, and how sensitive answers get held for review.
What it looks like, and the fix →Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.