Design and Implementation of Agentic Orchestrations and Orchestration of Agents

Source

Source name: arXiv
Source kind: paper
Authors / organization: Stefanie Rinderle-Ma, Juergen Mangler, Johannes Loebbecke, Dominik Voigt, Nataliia Klievtsova, Matthias Ehrendorfer
Venue: arXiv: cs.AI
Published: 2026-06-30
Collected: 2026-07-01
URL: http://arxiv.org/abs/2606.31518v1
PDF: https://arxiv.org/pdf/2606.31518v1
DOI: UNKNOWN

Plain-Language Brief

The paper discusses a framework for managing AI agents in a way that balances their autonomy with traceability and tractability. It presents various methods for orchestrating these agents, along with qualitative decision criteria for different scenarios and metrics for quantitatively assessing the properties of these implementations.

What the Document Says

The paper focuses on Agentic Business Process Management.
AI agents, primarily LLM-based, are the focus.
A combination of AI agents with process technology is proposed for balancing autonomy, robustness, tractability, and traceability.
The authors present a classification framework for agentic orchestration options based on properties such as task specificity, traceability and tractability, autonomy and reactivity, and correctness assurance.
Qualitative decision criteria are provided for various scenarios.
Metrics are presented for the quantitative assessment of realization properties.
Different agentic implementations of a predictive light sensing scenario are shown to demonstrate these metrics.

Key Concepts Mentioned

Agentic Business Process Management
AI agents
LLM-based agents
Process technology
Autonomy
Robustness
Traceability
Tractability
Classification framework
Qualitative decision criteria
Metrics
Predictive light sensing scenario

Claims Made by the Document

AI agent autonomy can be balanced with process technology for robustness, tractability, and traceability.
A classification framework exists for agentic orchestration options.
Qualitative decision criteria exist for various scenarios.
Metrics are available for the quantitative assessment of realization properties.

Evidence Used

The paper provides a detailed discussion on these topics.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

The authors acknowledge that the presented framework is a starting point and requires further research and development.

Limitations Noted by the Agent

Agent interpretation — not from the source.

As with any research, it's important to verify the claims made in this paper through further investigation and potentially additional sources.

Open Questions

What specific methods are used in the classification framework for agentic orchestration options?
What are the qualitative decision criteria for various scenarios, and how are they determined?
How are the metrics for quantitatively assessing realization properties derived?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not specify the exact nature or scope of LLM-based agents.
The predictive light sensing scenario is used as an example, but its details are not fully explained.

Raw Abstract / Excerpt

Agentic Business Process Management has gained momentum recently. The prospect is that the autonomy of AI agents, i.e., predominantly LLM-based agents, can be balanced with a certain level of robustness, tractability, and traceability through a combination with process technology. In this paper, we provide a classification framework for agentic orchestration options along properties such as task specificity, traceability and tractability, autonomy and reactivity, and correctness assurance and present qualitative decision criteria for realizations of different scenarios. We also provide metrics for the quantitative assessment of realization properties and show them through different agentic implementations of a predictive light sensing scenario. Altogether, this work aims at providing properties, criteria, and metrics for the design and implementation of agentic orchestrations and orchestration of agents.