# An Agentic AI for a New Paradigm in Business Process Development

arXiv: https://arxiv.org/abs/2507.21823 · HTML: https://mlsanity.com/paper/2507.21823/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 6 of 6)

- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [EvoFlow: Evolving Diverse Agentic Workflows On The Fly](https://mlsanity.com/paper/2502.07373/) (2025)
- [Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents](https://mlsanity.com/paper/2501.00881/) (2025)
- [AI Agentic workflows and Enterprise APIs: Adapting API architectures for the age of AI agents](https://mlsanity.com/paper/2502.17443/) (2025)
- [Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI](https://mlsanity.com/paper/2504.08148/) (2025)
- [Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform](https://mlsanity.com/paper/2501.00750/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Formal Framework for Declarative Agentic AI in Business Process Analysis](https://mlsanity.com/paper/2606.15291/) (2026)
- [The Integrator Advantage: Controlled Agentic AI for Small and Medium-Sized Companies](https://mlsanity.com/paper/2606.16649/) (2026)

## Abstract (arXiv)

Artificial Intelligence agents represent the next major revolution in the continuous technological evolution of industrial automation. In this paper, we introduce a new approach for business process design and development that leverages the capabilities of Agentic AI. Departing from the traditional task-based approach to business process design, we propose an agent-based method, where agents contribute to the achievement of business goals, identified by a set of business objects. When a single agent cannot fulfill a goal, we have a merge goal that can be achieved through the collaboration of multiple agents. The proposed model leads to a more modular and intelligent business process development by organizing it around goals, objects, and agents. As a result, this approach enables flexible and context-aware automation in dynamic industrial environments.