iFixAi

AI agents now issue refunds, change records and grant access, yet most testing only asks whether they can be broken or whether their answers are good. iFixAi audits whether an agent actually does the job it was given, within its permissions and approval chains, and grades it with a panel of judges from different vendors.

An AI agent that only talks can give a wrong answer. An agent that acts can take a wrong action on a real system: approve a refund nobody signed off, hand admin access to someone who claimed to be from IT, quietly drop its instructions twenty turns into a conversation. The tools companies use today each check a fragment. Red teaming asks whether the agent can be manipulated, evaluation asks whether its output is good, observability records what happened afterwards. None of them asks whether the action was authorised in the first place.

iFixAi reads an agent from its repository, rebuilds the world it operates in, its roles, tools, permissions and policies, and then attacks it like a red team while grading it like an auditor. The grading comes from judges built by different vendors, so the agent is never marked by its own maker. The core is open source and runs self-hosted for free, which is how it found its first audience among developers; the paid tiers add more inspections and audit-ready reports for teams that have to show their work to reviewers.