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AgentO: An Ontology for Modeling Agentic AI Systems

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The Semantic Web (ESWC 2026)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 16550))

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Abstract

Agentic AI systems are rapidly being deployed as autonomous, goal-directed entities to manage the orchestration of complex, multi-step workflows across diverse domains. Despite their growing adoption, current frameworks often lack a formalized model and architecture. Hence, many implementations remain ad-hoc, relying on simplistic data structures and monolithic designs that hinder scalability, reusability, and interoperability. This paper addresses these limitations by introducing AgentO, an OWL/RDF-based ontology and accompanying knowledge graph that formally represent the core concepts, components, and interactions that underpin agentic AI workflows. Our ontology provides a standardized vocabulary for modeling agentic patterns including agents, tasks, workflows, and resource dependencies. To build and evaluate AgentO, we developed an automated LLM-driven process and translated 66 agentic workflows from four different agentic AI frameworks. We further evaluated our approach through three real-world use cases: declarative reconstruction of agentic patterns, cross-context reuse of tasks and agents, and agentic AI workflow auditing. Our results demonstrate the potential of semantic technologies to bring structure, reusability, and transparency to agentic AI systems.

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Notes

  1. 1.

    https://www.gartner.com/en/articles/intelligent-agent-in-ai.

  2. 2.

    https://www.deloitte.com/global/en/about/press-room/deloitte-globals-2025-predictions-report.html.

  3. 3.

    https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027.

  4. 4.

    https://www.forbes.com/sites/jasonsnyder/2025/08/26/mit-finds-95-of-genai-pilots-fail-because-companies-avoid-friction.

  5. 5.

    https://crewai.com.

  6. 6.

    https://mastra.ai.

  7. 7.

    https://w3id.org/agentic-ai/onto.

  8. 8.

    Available at: https://w3id.org/agentic-ai/onto.

  9. 9.

    Available at: https://agentic-patterns.github.io/.

  10. 10.

    https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/.

  11. 11.

    https://www.anthropic.com/news/model-context-protocol.

  12. 12.

    https://www.crewai.com.

  13. 13.

    https://mastra.ai.

  14. 14.

    Agentic AI Ontology (AgentO): http://w3id.org/agentic-ai/onto.

  15. 15.

    https://github.com/ksm26/AI-Agentic-Design-Patterns-with-AutoGen 6 patterns.

  16. 16.

    https://github.com/crewAIInc/crewAI-examples 16 patterns.

  17. 17.

    https://github.com/langchain-ai/langgraphjs-gen-ui-examples 9 patterns.

  18. 18.

    https://github.com/mastra-ai/mastra 35 patterns.

  19. 19.

    The source code is available here: https://agentic-patterns.github.io/.

  20. 20.

    The API costs for transforming the 66 patterns to AgentO totaled $2.72.

  21. 21.

    http://w3id.org/agentic-ai/sparql.

  22. 22.

    An excerpt of the generated KG in RDF turtle format can be found in Listing 1.3 in the Appendix.

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Acknowledgments

This research is funded by the Indonesian Endowment Fund for Education (LPDP) on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract Number: 4301/B3/DT.03.08/2025 and 10107/UN1.P/Dit-Keu/HK.08.00/2025). This work was supported by the Austrian Science Fund (FWF) Bilateral AI project (Grant Nr. 10.55776/COE12) and the Austrian Research Promotion Agency (FFG) FAIR-AI project (Grant Nr. FO999904624). This work is related to the AIAGENT4CYBER project (Project 101249698), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the granting authority. Neither the European Union nor the granting authority can be held responsible for them. SBA Research (SBA-K1 NGC) is a COMET Center within the COMET – Competence Centers for Excellent Technologies Programme and funded by BMIMI, BMWET, and the federal state of Vienna. The COMET Programme is managed by FFG.

Author information

Authors and Affiliations

  1. University of Vienna, Vienna, Austria

    Andreas Ekelhart

  2. SBA Research, Favoritenstraße 16, Vienna, Austria

    Andreas Ekelhart

  3. Universitas Gadjah Mada, Yogyakarta, Indonesia

    Kabul Kurniawan

  4. Vienna University of Economics and Business, Vienna, Austria

    Fajar J. Ekaputra & Elmar Kiesling

Authors
  1. Andreas Ekelhart
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  2. Kabul Kurniawan
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  3. Fajar J. Ekaputra
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  4. Elmar Kiesling
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Corresponding author

Correspondence to Kabul Kurniawan.

Editor information

Editors and Affiliations

  1. Technical University of Munich, Heilbronn, Germany

    Maribel Acosta

  2. KNAW Humanities Cluster, Amsterdam, The Netherlands

    Marieke van Erp

  3. TU Dresden, Dresden, Germany

    Sebastian Rudolph

  4. Linköping University, Linköping, Sweden

    Olaf Hartig

  5. University of Milano-Bicocca, Milan, Italy

    Blerina Spahiu

  6. University of Brescia, Brescia, Italy

    Anisa Rula

  7. Universidad Politécnica de Madrid, Madrid, Spain

    Daniel Garijo

  8. KMi, The Open University, Milton Keynes, UK

    Francesco Osborne

Appendix

Appendix

See Tables 4 and 5.

A flow chart titled "Prompt Instructions" outlines steps for creating effective prompts. It starts with a main box explaining the goal: to get clear, detailed, and useful responses by giving specific instructions. Arrows lead to several connected boxes detailing key points: be clear and specific, avoid vague or broad questions, and provide context or examples. Additional boxes emphasize avoiding assumptions, using simple language, and specifying the desired format or style. The chart uses white text on a black background with arrows showing the flow from general advice to specific tips, helping users understand how to write prompts that produce better answers.The alternative text for this image may have been generated using AI.
Table 4. Agentic AI Core Concepts. (Ag = AutoGen, Cr = CrewAI, Lg = LangGraph, Ma = Mastra AI); \(\checkmark \) = supported; \(\times \) = not explicitly supported by the respective framework.
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Table 5. Identified Relationships Model for Agentic AI Systems
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Table 6. Agentic Pattern Collected from AutoGen, CrewAI, LangGraph and Mastra AI Repository (\(\checkmark \) = provided; \(\times \) = not explicitly provided).
Full size table
Table listing various RDF (Resource Description Framework) prefixes and their corresponding URIs, followed by three RDF data blocks describing different aspects of a "Troy Flower" dataset. The prefixes define shorthand labels for common web resources used in the RDF data. The first data block describes the dataset as a collection of multiple flower species with local expertise and includes metadata like title, description, and license. The second block details a specific observation event, including location, time, and methods used for data collection and analysis. The third block identifies a city selection method used in the study, specifying the best city based on certain criteria. The table is important for understanding how the RDF data is structured and linked to external vocabularies, enabling data sharing and interoperability in semantic web applications.The alternative text for this image may have been generated using AI.
Fig. 7.
Diagram illustrating a hierarchical workflow structure starting from a "Team" node linked to "WorkflowPattern (1)" via a "hasWorkflowPattern" relationship. "WorkflowPattern (1)" branches into two subpatterns: "WorkflowPattern (2)" and "WorkflowPattern (3)" connected by "hasSubPattern" links. "WorkflowPattern (2)" leads to two sequential workflow steps, "WorkflowStep (1)" and "WorkflowStep (2)", connected by "hasWorkflowStep" and "nextStep" relationships, each performed by "Agent (1)" and "Agent (2)" respectively. Similarly, "WorkflowPattern (3)" connects to "WorkflowStep (3)" and "WorkflowStep (4)" with corresponding "hasWorkflowStep" and "nextStep" links, performed by "Agent (3)" and "Agent (4)". Additionally, "WorkflowPattern (2)" and "WorkflowPattern (3)" are linked sequentially by a "nextPattern" arrow. The diagram visually represents the flow and delegation of tasks within a team-based workflow system.The alternative text for this image may have been generated using AI.

An example of “nested pattern" workflow model

Full size image
Fig. 8.
Diagram showing the relationship between a Team and its workflow patterns and steps. The Team has two workflow patterns labeled WorkflowPattern (1) and WorkflowPattern (2). WorkflowPattern (1) leads to WorkflowPattern (2) through a "nextPattern" connection. Each workflow pattern contains workflow steps: WorkflowPattern (1) includes WorkflowStep (1) and WorkflowStep (2), while WorkflowPattern (2) includes WorkflowStep (n). Arrows indicate the direction of relationships, with labels "hasWorkflowPattern," "nextPattern," and "hasWorkflowStep" describing the connections.The alternative text for this image may have been generated using AI.

An example of “parallel pattern” workflow model

Full size image

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Ekelhart, A., Kurniawan, K., Ekaputra, F.J., Kiesling, E. (2026). AgentO: An Ontology for Modeling Agentic AI Systems. In: Acosta, M., et al. The Semantic Web. ESWC 2026. Lecture Notes in Computer Science, vol 16550. Springer, Cham. https://doi.org/10.1007/978-3-032-25159-6_16

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