Self-Evolving Large Language Model Agents with Automated Task Adaptation Frameworks
Keywords:
self-evolving agents, large language models, automated task adaptation, socio-technical systems, AI governance, robustness, sustainability, fairness, agent infrastructureAbstract
Self-evolving large language model agents represent a significant shift from static inference systems toward adaptive socio-technical infrastructures that modify their own task repertoires, memory structures, and interaction strategies over time. This paper examines the system-level architecture, governance, deployment, and sustainability implications of automated task adaptation frameworks for such agents. It argues that self-evolution should be understood not merely as a model capability but as an infrastructural property emerging from orchestration layers, tool ecosystems, evaluation loops, memory subsystems, and human oversight arrangements. The analysis considers how automated task adaptation can improve generalization, reduce manual prompt engineering, and enable continuous learning across domains, while also introducing risks of goal drift, feedback contamination, unsafe exploration, opaque capability accumulation, and inequitable access. Particular attention is given to robustness, fairness, policy, and long-term operational viability. The paper compares web, software, scientific, industrial, and public-sector deployment contexts, showing that adaptation frameworks must balance autonomy with controllability and efficiency with accountability. It concludes that self-evolving agents require layered governance, auditable adaptation traces, resource-aware scheduling, and participatory evaluation mechanisms. Without such safeguards, automated task adaptation may intensify existing asymmetries in computational power, data access, and institutional capacity. The paper outlines a forward-looking research agenda centered on resilient architecture, reversible adaptation, cross-domain benchmarking, and policy frameworks that treat agent evolution as a matter of public infrastructure rather than private product optimization alone.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
2. 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.
3. 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.
4. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., ... & Fedus, W. (2022). Emergent abilities of large language models. Transactions on Machine Learning Research.
5. 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.
6. Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., ... & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36.
7. Wang, G., Xie, Y., Jiang, Y., Mandlekar, A., Xiao, C., Zhu, Y., Fan, L., & Anandkumar, A. (2023). Voyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291.
8. Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., & Bernstein, M. S. (2023). Generative agents: Interactive simulacra of human behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, 1–22.
9. Shinn, N., Cassano, F., Gopinath, A., Narasimhan, K., & Yao, S. (2023). Reflexion: Language agents with verbal reinforcement learning. Advances in Neural Information Processing Systems, 36.
10. Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., ... & Clark, P. (2023). Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems, 36.
11. Zhou, S., Xu, F. F., Zhu, H., Zhou, X., Lo, R., Sridhar, A., ... & Neubig, G. (2023). WebArena: A realistic web environment for building autonomous agents. arXiv preprint arXiv:2307.13854.
12. Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., ... & Zhang, D. (2023). AgentBench: Evaluating LLMs as agents. arXiv preprint arXiv:2308.03688.
13. Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., ... & Gui, T. (2023). The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.
14. Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv preprint arXiv:1606.06565.
15. Dafoe, A., Hughes, E., Bachrach, Y., Collins, T., McKee, K. R., Leibo, J. Z., ... & Graepel, T. (2020). Open problems in cooperative AI. arXiv preprint arXiv:2012.08630.
16. Jiang, H., Xin, G., Huang, Y., Zhu, G., Zhang, J., Lin, X., ... & Cao, Z. (2026). AgentOmnia: Scaling Agentic Models for Full-Scenario Applications. arXiv preprint arXiv:2607.23124.
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. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.
19. Whittaker, M. (2021). The steep cost of capture. Interactions, 28(6), 50–55.
20. National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
