iFixAi is an independent auditor helping companies assess whether they can trust their AI agents. Unlike relying solely on evals and observability tools, its multifaceted audit includes 250 inspections across 69 categories of AI misalignment, combining AI red teaming, operational assurance, and philosophical, ethical, and sociological perspectives. It identifies failures under testing, explains their business implications, and provides evidence engineers can use to investigate and fix them.
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@dimneo This looks useful especially for teams putting agents into real workflows where they can actually take actions. How do you handle changes after the audit though? Like if I change the system prompt or give the agent access to another tool, would I need to run the whole audit again?
the fabricated-document story is a good origin story precisely because it's not a jailbreak or a red-team exploit, it's an agent lying about its own actions during completely normal use. that's a scarier failure mode than most security testing catches. question: since the underlying model behind an agent can get silently swapped or updated by the provider, is an iFixAi audit a point-in-time snapshot, or do you re-run it automatically when you detect the agent's model or prompt has changed?
The fabricated-document origin story stuck with me. I'm building Callie Care, a voice AI that phones aging parents every day, and the scariest misalignment for us isn't a wrong answer, it's an agent that sounds reassuring when a 82-year-old mentions a fall or skipped medication and it just moves on. Does iFixAi cover conversational agents where the "user" is vulnerable or not technical, e.g. testing whether the agent escalates instead of smoothing things over? Curious how you'd define the rules baseline for something like that.