Autonomous simulated testing and benchmarking framework for agentic ai systems

Abstract

A computer-implemented method and system are disclosed for simulation-based testing and benchmarking of an agentic artificial intelligence (AI) system. The method comprises binding a simulation agent to one or more tool-access interfaces of the agentic AI system to replace external tools, intercepting requests emitted through the interfaces, and generating protocol-compliant responses using synthetic data and a simulated environment. The simulation agent executes healthy and fault-inserted task runs within the synthetic environment and generates a performance vector comprising task-completion rate, accuracy, efficiency, resilience, and fault-recovery metrics. The system is configured for maintaining a simulation registry, orchestrating resource allocation using reinforcement-learning policies, and applying an autonomous feedback pipeline for continuous refinement. In some embodiments, the simulation agent operates in a stealth observation mode to train surrogate tool models, enabling privacy-compliant, closed-loop simulation.