Agentic AI Systems: Architecture, Applications, and a Case Study of OpenClaw
Vimal Pandey
Scholar, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Nimish Jaiswal
Scholar, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Mohd. Hamza Ansari
Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
Farheen Siddiqui
Assistant Professor, Department of Computer Science & Engineering, Shri Ramswaroop Memorial University, Deva Road, Lucknow
📌 DOI: https://doi.org/10.63920/tjths.52016
🔑 Keywords: Agentic AI, Autonomous Agents, Large Language Models, AI Systems Architecture, OpenClaw, Multi-Agent Systems, Reinforcement Learning
📅 Publication Date: 07 April 2026
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This work is licensed under a Creative Commons Attribution 4.0 International License
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Abstract:
Artificial intelligence has undergone a paradigm shift from narrowly scoped, rule-based systems toward autonomous, goal-directed agents capable of planning, reasoning, and executing multi-step tasks with minimal human intervention. This paper examines the architecture, technical foundations, and real-world applications of Agentic AI, a class of systems that combines large language models (LLMs), reinforcement learning, retrieval-augmented generation (RAG), and tool-use frameworks to create intelligent agents that operate autonomously within complex environments. We analyze the layered architecture of such systems, encompassing perception, planning, memory, reasoning, and feedback mechanisms, and survey the machine learning techniques that underpin their decision-making capabilities. A detailed case study of Open Claw, an agentic AI framework designed for autonomous software engineering tasks, is presented to illustrate how theoretical architectures translate into practical, deployable systems. Our analysis reveals that while agentic AI holds transformative potential for industries ranging from software development to healthcare and enterprise automation, significant challenges persist around safety, alignment, and computational cost. This paper contributes a structured understanding of agentic AI systems suitable for undergraduate researchers and practitioners entering this rapidly evolving field.
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📖 How to Cite
Vimal P., Nimish J., Mohd. Hamza A., Farheen S. (2026). Agentic AI Systems: Architecture, Applications, and a Case Study of OpenClaw. TEJAS J. Technol. Humanit. Sci.,, Vol. 05, Issue 02. https://doi.org/10.63920/tjths.52016
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References
[1] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, May 2015.
[2] OpenAI, “GPT-4 Technical Report,” arXiv:2303.08774, 2023.
[3] S. Weng, “LLM-powered autonomous agents,” Lil’Log, Jun. 2023. [Online]. Available: Blog Link
[4] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.
[5] G. Weiss, Ed., Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, 1999.
[6] A. S. Rao and M. P. Georgeff, “BDI agents: From theory to practice,” in Proc. Int. Conf. Multi-Agent Systems, 1995, pp. 312–319.
[7] A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
[8] T. Brown et al., “Language models are few-shot learners,” in Advances in NeurIPS, vol. 33, 2020.
[9] J. Wei et al., “Chain-of-thought prompting elicits reasoning in large language models,” in Advances in NeurIPS, 2022.
[10] S. Yao et al., “ReAct: Synergizing reasoning and acting in language models,” in Proc. ICLR, 2023.
[11] T. Schick et al., “Toolformer: Language models can teach themselves to use tools,” arXiv:2302.04761, 2023.
[12] P. Lewis et al., “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in NeurIPS, vol. 33, 2020.
[13] Q. Wu et al., “AutoGen: Enabling next-gen LLM applications via multi-agent conversation,” arXiv:2308.08155, 2023.
[14] Anthropic, “Claude’s model specification,” 2024. [Online]. Available: Website
[15] E. Zelikman, Y. Wu, J. Mu, and N. Goodman, “STaR: Bootstrapping reasoning with reasoning,” in Advances in NeurIPS, 2022.
[16] D. Significant Gravitas, “AutoGPT: An autonomous GPT-4 experiment,” GitHub, 2023. [Online]. Available: GitHub Link
[17] N. Akhtar et al., “AI-driven intelligent resume recommendation engine,” IJSRSET, vol. 12, no. 3, pp. 1141–1155, June 2025. DOI
[18] K. R. Poranki, Y. Perwej, and N. Akhtar, “Integration of SCM and ERP for competitive advantage,” Research Journal of Science & IT Management, vol. 4, no. 5, pp. 17–24, 2015.
[19] Y. Perwej, “Evaluation of deep learning miniature in soft computing,” IJARCCE, vol. 4, no. 2, pp. 10–16, Feb. 2015. DOI
[20] Y. Perwej and F. Parwej, “Neuroplasticity approach in artificial neural networks,” IJSER, vol. 3, no. 6, pp. 1–9, 2012. DOI
[21] Y. Perwej, “Bidirectional LSTM neural network for Arabic documents,” TMLAI, vol. 3, no. 1, pp. 16–27, 2015. DOI
[22] Y. Perwej, “Unsupervised feature learning for text pattern analysis,” in Proc. IEEE ICACTA, 2022. DOI
[23] M. Wooldridge and N. R. Jennings, “Intelligent agents: Theory and practice,” Knowledge Engineering Review, vol. 10, no. 2, pp. 115–152, 1995.
[24] G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv:1503.02531, 2015.
[25] J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in NeurIPS, vol. 33, 2020.
[26] L. Ouyang et al., “Training language models to follow instructions with human feedback,” in Advances in NeurIPS, vol. 35, 2022.
[27] K. Cobbe et al., “Training verifiers to solve math word problems,” arXiv:2110.14168, 2021.
[28] R. Nakano et al., “WebGPT: Browser-assisted question answering with human feedback,” arXiv:2112.09332, 2021.
[29] C. Guo et al., “On calibration of modern neural networks,” in Proc. ICML, 2017, pp. 1321–1330.
[30] S. Bubeck et al., “Sparks of artificial general intelligence,” arXiv:2303.12528, 2023.
[31] A. Madaan et al., “Self-refine: Iterative refinement with self-feedback,” in Advances in NeurIPS, 2023.