Vision-Language Agents for Adaptive Task Planning in Automated Software Workflows
Keywords:
vision-language agents; automated software workflows; adaptive task planning; multimodal interfaces; governance; robust systems; socio-technical infrastructure; human oversightAbstract
Vision-language agents combine visual perception, linguistic reasoning, and tool use to plan and execute tasks inside graphical and code-based software environments. This paper examines their role in adaptive task planning for automated software workflows, focusing on system architecture, infrastructure, governance, robustness, fairness, sustainability, and policy. It argues that the central challenge is not merely model capability but the alignment of stochastic multimodal reasoning with deterministic workflow semantics, compliance requirements, and human accountability. The paper develops a layered account of perception, state representation, planning, execution, verification, and observability, showing how each layer introduces trade-offs between flexibility and control. It then analyzes deployment concerns, including latency, cost, energy, dependency management, and technical debt, and considers how model cards, datasheets, audit trails, and human oversight can support responsible automation. The discussion extends to cross-domain comparisons with embodied agents, robotic process automation, and multiagent systems, illustrating where vision-language planning offers advantages and where it creates new failure modes. The conclusion proposes that sustainable adoption depends on treating agents as governed socio-technical actors embedded in institutional processes rather than as standalone software components. The paper contributes a system-level research agenda for designing adaptive, auditable, and equitable vision-language automation in software workflows.
References
1. Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. Cambridge University Press.
2. Wooldridge, M. (2009). An introduction to multiagent systems (2nd ed.). Wiley.
3. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
4. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
5. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
6. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, 139, 8748-8763.
7. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
8. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2023). ReAct: Synergizing reasoning and acting in language models. International Conference on Learning Representations.
9. Lei, B., Kang, W., Zhang, Z., Chen, W., Xie, X., Zuo, S., ... & Ding, C. (2026). InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction. Advances in Neural Information Processing Systems, 38, 34494-34520.
10. Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., ... & Florence, P. (2023). PaLM-E: An embodied multimodal language model. Proceedings of the 40th International Conference on Machine Learning, 202, 8469-8484.
11. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36, 68539-68551.
12. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., ... & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744.
13. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503-2511.
14. Breck, E., Cai, S., Nielsen, E., Salib, M., & Sculley, D. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. 2017 IEEE International Conference on Big Data, 1123-1132.
15. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., ... & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220-229.
16. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92.
17. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623.
18. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
19. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
20. Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, T., Zhu, Y., Fan, L., & Anandkumar, A. (2023). Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Artificial Intelligence and Machine Learning Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.