WHERE AGENTIC AI BREAKS HERE
Hallucinated confidence on fraud-adjacent decisions
Warranty claims, exception requests, and edge-case approvals slip through at speed with confidence the underlying evidence does not support
Telemetry and input poisoning
Adversarial sensor inputs and tampered claim evidence steer agentic AI toward unsafe routing, maintenance, or approval decisions
Driver and interface prompt injection
In-cab interfaces and free-text channels carry hidden instructions. Nudged agentic AI issues directives safety policy would never sanction
Agentic AI generates the fleet decision with detailed analysis.
Routing, maintenance schedules, warranty assessments, fraud-risk scores, and design feedback all produced inside the autonomous loop.
Disseqt scores every decision in real time against safety and policy.
Each output checked for confidence, fraud risk, telemetry integrity, and consistency with comparable decisions across the fleet.
Flagged decisions go to operations with root-cause analysis.
Accepted recommendations and overrides both feed back into the model, turning every human review into reinforcement signal.
Closed-loop audit trail for safety regulators, insurers, and quality.
DOT, DVSA, EU AI Act, and insurer evidence assembled from live fleet decisions, traceable across the workflow lifecycle.
Real-time scoring on every decision
Routing, maintenance, warranty, and design decisions all scored as they are produced, before they reach the operational team.
Fraud and integrity signals at speed
Telemetry poisoning, fraudulent claims, and tampered evidence caught release-over-release, with the testing record to defend it.
Closed-loop feedback into the model
Accepted recommendations and overrides both feed reinforcement signal back to the agent, sharpening accuracy over time.
One pattern, adjacent workflows
The same assurance shape reused across warehouse, logistics, and adjacent operational AI workflows.
