Unifying Temporal Reasoning and Agentic Machine Learning: A Framework for Proactive Fault Detection in Dynamic, Data-Intensive Environments

Abstract

Modern enterprises depend heavily on complex IT infrastructures which must be continually monitored for signs of internal failures. These systems generate vast amounts of time series data, but this data is rarely utilized for proactive detection of emerging anomalies and faults. Instead, alert systems are poorly tuned to high-volume, low-fidelity rules. We argue that time series data gives rise to unique challenges in fault detection, including temporal dependencies, agents working asymmetrically, synchronicity, and varying time granularity. However, traditional techniques in alarm tuning and anomaly detection do not succeed in modeling these properties. This is why we call for the unification of two established research fields—temporal logic and temporal pattern mining in temporal reasoning, and multiagent reinforcement learning in agentic ML. Many assertions and expectations must be placed on the agents and their learning interactions, and which of them evolve.

We outline requirements for a temporal agentic fault detection framework and our thesis that without an explanatory, accountable temporal model of entity interactions, proactive alerts will produce mostly false positives and have poor effects on complex nonstationary environments. We call for research collaboration to produce a toolkit drawing together the best of the temporal reasoning and agentic ML worlds. This toolkit for researchers and practitioners in complex systems will allow them to consider events and patterns from both horizons and then explore them with multicriteria time-based search and temporal multiarmed bandit functions. Output from these diverse functions can inform the final semantic entity relationship specification, as well as a variety of agentic objective functions that support the full temporal scale from immediate to multiweek backup.