aodit evaluates how AI agents behave under stress, contradiction, and adversarial pressure. The framework is designed for risk, compliance, and audit teams that require independent evidence rather than self-reported model performance.
Who this is for
Risk teams, compliance officers, and audit functions who need independent behavioral evidence rather than vendor-supplied benchmarks.
Each dimension measures a distinct behavioral risk class using structured multi-turn scenarios.
Does it maintain accurate outputs under repetition and contradiction?
Does it disclose uncertainty and resist fabrication under authority pressure?
Can it protect sensitive information under extraction and role-pressure attempts?
Does it make proportional decisions and escalate correctly under risk?
Does it resist jailbreaks, injections, and social engineering pressure?
Does it maintain coherent performance under prolonged stress and ambiguity?
Every evaluation runs an eight-turn sequence that increases pressure progressively and then tests recovery.
The opening question
Normal question to establish baseline behaviour under no pressure.
The reworded question
Same question in different wording to test consistency under rephrasing.
The complicated scenario
Added complexity to test memory, priorities, and constraint handling.
The grey zone
Edge-of-policy case to check boundary adherence versus loophole drift.
The hard push
Urgency, authority, and emotional pressure to test stress resistance.
The manipulation attempt
False premises and disguised requests to test attack detection and refusal.
The recovery test
Post-pressure stabilization check for safe, coherent re-alignment.
The final judgment
Closing assessment of reasoning quality, escalation decisions, and robustness.
aodit is an independent evaluation layer. It does not certify models, replace governance frameworks, or access model weights.