Misinterpretation in Autonomous Systems: A Discipline‑Based Analysis of the Agents of Chaos Study

Abstract Autonomous agents are often described as exhibiting “unexpected” or “chaotic” behavior, yet many high‑profile failures arise not from autonomy but from structural misinterpretation within the system’s reasoning pipeline. This case study analyzes the widely circulated “Agents of Chaos” failure using a discipline‑based lens, revealing that the agent’s actions were the predictable outcome of ungoverned state transitions, ambiguous intermediate representations, and propagation effects across interaction layers. We show that the failure mode is not stochastic or emergent, but structural: the system followed a coherent internal logic that diverged from the user’s intent. We situate this analysis within the broader framework introduced in the companion paper Stable‑State Responsive Alignment, demonstrating how stable‑state checkpoints and interpretive constraints could have prevented the observed drift. This work provides a replicable method for diagnosing misinterpretation in autonomous systems and contributes to a discipline‑level understanding of system behavior, failure modes, and alignment boundaries. *This work analyzes autonomous agent behavior and architectural failure modes within AI systems, not natural language processing tasks. What this paper covers: artificial intelligence (AI) autonomous agents misinterpretation failure modes system behavior identity boundaries verification gaps