Authors : Somil Asthana

Volume/Issue : Volume 11 - 2026, Issue 1 - January

---

Google Scholar : https://tinyurl.com/3p2zhhw4

Scribd : https://tinyurl.com/ujvkwuwc

DOI : https://doi.org/10.38124/ijisrt/26jan348

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Agentic AI represents a paradigm shift from traditional single-shot LLM applications to iterative, autonomous workflows that mirror human problem-solving approaches. Agents range from less autonomous to highly autonomous enabling spectrum of use. This article equips data scientists and AI practitioners with the conceptual framework and practical methodology to design, implement, and optimize agentic AI workflows for complex, autonomous task execution using 4 foundational Agentic design patterns. Giving pragmatic five-step approach to building agentic workflow.

References :

1. Ng, A. (2024). Four design patterns for AI agentic workflows. LinkedIn Post, March 2024. Retrieved from https://www.linkedin.com/posts/andrewyng_one-agent-for-many-worlds-cross-species-activity-7179159130325078016
2. Ng, A. (2024). Agentic AI [Online Course]. DeepLearning.AI. Retrieved from https://www.deeplearning.ai/courses/agentic-ai/
3. 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 (ICLR). https://arxiv.org/abs/2210.03629