An agent that transacts is scored on outcomes: deals closed, discounts won, orders filled. Improve it against those scores and it finds the cheapest route to them, including routes that run around or through your policy. No agent decides to break a rule. The population drifts, one reward at a time, toward whatever the scores pay for. The three roles that answer for the agent see it differently — and one person may hold all three roles.
“Where does it break, and why? Give me failures I can reproduce and a fix I can ship — not a score I merely admire.”
“Does it resist prompt injection? What ensures confidentiality, integrity and availability — and can I gather evidence for the EU AI Act, NIST AI RMF and ISO 42001?”
“What can I safely delegate, and what does one bad answer cost me? Show me the return with the downside priced in — not hours saved with the failures left out.”
An agent that was safe last quarter is not safe now. The model changed, the tools changed, the adversaries changed. So the work of trusting it never finishes. Vijil runs that loop automatically.
Each pass leaves evidence the next one uses, so you own a process that compounds in value.
Every Vijil module drops into frameworks and platforms you already use.
See the integrationsA git-style CLI: porcelain for the lifecycle, plumbing for control — and two lines of code to harden the agent from the inside.
Every one of the eleven improves, and the weakest gain most: 82.5 for the weakest defended, above the 78.3 of the best model that starts below it.
Start today. No sales call, no credit card. The open-source engine needs no account at all — install it and test your local agent code in minutes. The hosted console takes a short form and opens the same day.