arXiv:2607. 16738v1 Announce Type: new Abstract: AI-Augmented Business Process Management Systems (ABPMS) enhance traditional BPMS by leveraging advanced AI techniques to define, execute, and monitor complex process structures.
arXiv:2604. 22455v2 Announce Type: replace Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries.
We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine. A process harness places a policy-governed agentic layer around a deterministic workflow engine, intercepting designated control points to contribute reasoning, adaptation, and oversight while the engine retains structural authority over the process.
arXiv:2606. 10044v1 Announce Type: new Abstract: Businesses are increasingly adopting AI-enabled tools to improve productivity, reduce costs, and enhance products and services.
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data‑driven decisions made during execution. It proposes framing the problem as a chance‑constrained optimization and introduces two formulations: a decomposed two‑stage approach (planning to minimize superfluous activities under a feasibility constraint, followed by scheduling to minimize makespan) and an integrated single‑stage approach. Experiments on two real‑world and one synthetic dataset show that the integrated method achieves better makespans but struggles with scalability, whereas the decomposed method scales to larger settings.
The paper addresses the challenge of scheduling business process activities when the exact sequence of required tasks is uncertain due to data-driven decisions made during execution. It proposes framing the problem as a chance-constrained optimization and introduces two formulations: a decomposed two-stage approach that first minimizes expected superfluous activities under a feasibility constraint and then schedules to minimize makespan, and an integrated approach that combines planning and scheduling into a single model. Experiments on two real-world and one synthetic dataset show that the integrated approach achieves better makespans but struggles with scalability, whereas the decomposed approach scales to larger settings.
arXiv:2606. 15291v1 Announce Type: new Abstract: Agentic AI opens new opportunities for automating Business Process (BP), enabling autonomous decision-making and dynamic adaptation.
arXiv:2606. 27188v1 Announce Type: new Abstract: We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow engine.
arXiv:2607. 17331v1 Announce Type: new Abstract: Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries.
arXiv:2502. 19135v2 Announce Type: replace Abstract: We present PLANTOR, a framework for generating and executing multi-robot task plans from natural-language task descriptions through LLM-assisted knowledge-base construction.
arXiv:2511. 05355v3 Announce Type: replace Abstract: Flow matching (FM) has shown promising results in data-driven planning.
arXiv:2606. 23797v1 Announce Type: cross Abstract: Graph and multi-agent orchestration frameworks make production large language model (LLM) workflows practical, but they do not by themselves solve conversational continuity when users maintain several interdependent objectives.
arXiv:2607. 21414v1 Announce Type: new Abstract: In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula.