Predicting the next activity in an ongoing process is one of the most common tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and aid both in risk mitigation and strategic decision-making. Existing state-of-the-art AI models for BPM do not fully capitalize on available semantic information within process event logs. As current advanced AI-BPM systems provide semantically richer textual data, the need for new adequate models grows. To address this gap, we develop SNAP—a novel system that utilizes LLMs by constructing narratives and semantic contextual stories for historical event logs, which are then used to generate precise and actionable predictions for the ongoing process. SNAP was evaluated on six benchmark datasets, where it demonstrated significant performance improvements over eleven SOTA models, particularly on datasets with high levels of semantic content. This work showcases the potential of integrating LLMs in BPM and outlines a clear path toward future deployment, emphasizing the relevance and innovation of our approach within the broader AI application landscape.

Large Language Models (LLMs) are increasingly deployed within agentic systems—collections of interacting, LLM-powered agents that execute complex, adaptive workflows using memory, tools, and dynamic planning. While enabling powerful new capabilities, these systems also introduce unique forms of uncertainty stemming from probabilistic reasoning, evolving memory states, and fluid execution paths. Traditional software observability and operations practices fall short in addressing these challenges.This paper presents our vision of AgentOps: a comprehensive framework for observing, analyzing, optimizing, and automating operation of agentic AI systems. We identify distinct needs across four key roles—developers, testers, site reliability engineers (SREs), and business users—each of whom engages with the system at different points in its lifecycle. We present the AgentOps Automation Pipeline, a six-stage process encompassing behavior observation, metric collection, issue detection, root cause analysis, optimized recommendations, and runtime automation. Throughout, we emphasize the critical role of automation in managing uncertainty and enabling self-improving AI systems—not by eliminating uncertainty, but by taming it to ensure safe, adaptive, and effective operation.

The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or from sensors. When creating diagnosis networks, for example, unstructured analysis algorithms can be run on the historical text descriptions or images of previous cases so as to extract data for learning Bayesian network parameters, but such derived data has inherent uncertainty associated with it due to the nature of such algorithms. Because of the inability of current Bayesian network parameter learning algorithms to incorporate such uncertainty, common approaches either ignore this uncertainty, thus reducing the resulting accuracy, or completely disregard such data. We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism. We present a generalization of the Expectation Maximization parameter learning algorithm that enables it to handle any historical data with likelihood-evidence-based uncertainty, as well as an empirical validation demonstrating the improved accuracy and convergence enabled by our approach. We also prove that our extended algorithm maintains the convergence and correctness properties of the original EM algorithm, while explicitly incorporating data uncertainty in the learning process.

Efficiency is critical to the profitability of software maintenance and support organizations. Managing such organizations effectively requires suitable measures of efficiency that are sensitive enough to detect significant changes, and accurate and timely in detecting them. Mean time to close problem reports is the most commonly used efficiency measure, but its suitability has not been evaluated carefully. We performed such an evaluation by mining and analyzing many years of support data on multiple IBM products. Our preliminary results suggest that the mean is less sensitive and accurate than another measure, percentiles, in cases that are particularly important in the maintenance and support domain. Using percentiles, we also identified statistical techniques to detect efficiency trends and evaluated their accuracy. Although preliminary, these results may have significant ramifications for effectively measuring and improving software maintenance and support processes.