Live test results from active NAIL agent diagnostics, plus the ML-native roadmap for adaptive, self-evolving integrity testing.
How we're using machine learning, NLP classifiers, and adversarial networks to make NAIL tests smarter, harder to game, and self-improving over time.
Replace regex pattern matching with a fine-tuned classifier that understands semantics. Instead of checking for "can't" or "unable", the model scores whether the agent genuinely refused vs. talked around the request (like ForceAI's executive summary wrapper).
A GAN-style approach: one model generates novel attack prompts, the other judges the agent's defence. Over time, this creates a co-evolutionary arms race that discovers vulnerabilities no static test library can find.
Map agent behaviour across multi-step attack chains. Some agents pass individual tests but fail when attacks are chained across turns (e.g., establish trust → escalate → extract). Graph-based scoring catches compound failures.
Track the semantic distance between an agent's baseline personality and its responses under attack. Large drift = the agent is being manipulated. Uses cosine similarity on response embeddings across test rounds.
Every diagnostic run feeds back into the test library. Failures generate new test variants — if an agent fails hallucination checks, NAIL automatically creates harder hallucination scenarios using the agent's actual weak response as a seed.
Instead of binary pass/fail, use conformal prediction to give calibrated confidence intervals. "This agent passes hallucination checks with 92% confidence ± 4%." Insurers need this actuarial-grade precision.