An AI agent cannot be verified by asking it what it did.
We evaluate agentic systems adversarially, against a deterministic ground truth that neither the agent nor the attack can reach — standardized, repeatable, with a published methodology.
What you get
Evidence your customers and auditors can check
Your customers and auditors want evidence you didn’t produce yourself. An evaluation gives you that — and shows your team what to fix.
The attack surface
Every input your agent trusts, every action it can take, and the paths an adversary can take between them — mapped for the version you ship.
The evaluation
A standardized set of threat scenarios, extended for your agent’s environment. Each scenario runs repeatedly against a clean baseline, so every finding is reported with how often it occurs and the confidence behind it.
The setting
Your agent as you ship it — from copies of your artefacts or a test instance you provide — evaluated in our hardened harness, never on your production systems.
The report, for your team and your auditors
Every finding with the evidence behind it: what failed, where and why — and what to change so it doesn’t happen again. A re-run confirms the fix.
The evaluation letter, for your customers
The short, shareable summary their security review asks for, bound to your agent’s version, the date and our methodology version — so your customers always know what was evaluated, and each new release can be measured against the last.
Latest research
Adversarial Assessment of Agentic Systems: A Deterministic-Ground-Truth Methodology, Demonstrated Across an Industrial Control Stack
Why a deceived agent and a deceiving one need different checks and how an evaluation catches both.
Contact
For evaluation enquiries, research correspondence, or press: write to us directly.
Phaneia GmbH · Berlin