Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.09696 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915773425909760 |
|---|---|
| author | Corneli, Joseph Danoff, Charles J. Puzio, Raymond S. Ayloo, Sridevi Belich, Sergio Wilkinson, Andre Tedeschi, Mary Mosley, Pauline |
| author_facet | Corneli, Joseph Danoff, Charles J. Puzio, Raymond S. Ayloo, Sridevi Belich, Sergio Wilkinson, Andre Tedeschi, Mary Mosley, Pauline |
| contents | Design patterns have been used in various fields of inquiry and endeavour to externalize procedural knowledge in a form that supports human reasoning and coordination. In this paper, we show that contemporary Large Language Model (LLM)-based systems can also read, generate, and reason with design patterns written in a structured template. We describe an experimental workflow in which patterns function as shared priors for action selection, reflection, and revision in hybrid human/agent settings. Drawing on the Active Inference Framework, we illustrate how patterns can guide agent behavior without fully prescribing it. This provides a proof of concept that pattern-capable agents can be created using now-standard software tools. We discuss implications for software development, education, business, and AI governance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Patterns for a New Generation: AI and Agents Corneli, Joseph Danoff, Charles J. Puzio, Raymond S. Ayloo, Sridevi Belich, Sergio Wilkinson, Andre Tedeschi, Mary Mosley, Pauline Human-Computer Interaction Design patterns have been used in various fields of inquiry and endeavour to externalize procedural knowledge in a form that supports human reasoning and coordination. In this paper, we show that contemporary Large Language Model (LLM)-based systems can also read, generate, and reason with design patterns written in a structured template. We describe an experimental workflow in which patterns function as shared priors for action selection, reflection, and revision in hybrid human/agent settings. Drawing on the Active Inference Framework, we illustrate how patterns can guide agent behavior without fully prescribing it. This provides a proof of concept that pattern-capable agents can be created using now-standard software tools. We discuss implications for software development, education, business, and AI governance. |
| title | Patterns for a New Generation: AI and Agents |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.09696 |