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Paper ID:IJSREM67151

From Foundations to Autonomous Ecosystems: A Comprehensive Survey of Agentic AI Architectures, Protocols, Socio-Technical Dynamics, and Future Trajectories

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67151

Volume:

10 |

9 |

Sep 2026

Pages:

1-19

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67151

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VG

Vipul Gangwani

School of Sciences (BCA)

KK Modi University Durg

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DMBA

Dr. Mohammed Bakhtawar Ahmed

School of Sciences (BCA)

KK Modi University Durg

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Abstract

The paradigm shift from passive Generative AI to proactive Agentic AI marks a pivotal evolution in artificial intelligence, transcending static content generation to enable autonomous, goal-directed task execution within dynamic environments. This comprehensive survey systematically bridges the historical divide between classical symbolic architectures and modern neural-generative lineages, formalizing a unified taxonomy that resolves conceptual retrofitting. By leveraging the Cognitive Architectures for Language Agents (CoALA) framework and a 10-dimension Cognitive Faculty matrix, we deconstruct the operational mechanics of multi-agent ecosystems across persistent memory hierarchies, interleaved reasoning-and-acting loops (e.g., ReAct, Tree of Thoughts), and dynamic tool utilization. Furthermore, we investigate the critical role of standardized inter-agent communication protocols (MCP, A2A, ACP, ANP, Agora) in facilitating robust orchestration within frameworks like AutoGen and CrewAI. Transitioning from theoretical foundations to practice, we provide deep empirical case studies in high-stakes domains, including 6G intelligent communications, automated software engineering, and clinical decision support. Crucially, this survey addresses the profound socio-technical implications of delegated autonomy through the MAD-BAD-SAD framework, analyzing emerging threat surfaces such as prompt injection, memory poisoning, and the "lethal trifecta." Finally, we critically evaluate the necessary shift from outcome-based to process-oriented benchmarking and outline future trajectories—encompassing lifelong learning, Green Agentic AI, and neuro-symbolic convergence—charting the rigorous path from isolated algorithmic agents toward Artificial General Intelligence (AGI).

KEYWORDS

Agentic AI Artificial General Intelligence (AGI) Cognitive Architectures Large Language Models (LLMs) Multi-Agent Systems (MAS) Interoperability Protocols Socio-Technical Systems Trustworthy AI.

Cite This Article

Vipul Gangwani, Dr. Mohammed Bakhtawar Ahmed, "From Foundations to Autonomous Ecosystems: A Comprehensive Survey of Agentic AI Architectures, Protocols, Socio-Technical Dynamics, and Future Trajectories," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 9, pp. 1-19, Sep 2026. https://doi.org/10.55041/IJSREM67151

Full Article/Conclusion

The emergence of Agentic AI represents a transformative milestone in the trajectory of artificial intelligence, redefining computational systems from passive responders to proactive, autonomous collaborators. As demonstrated throughout this comprehensive survey, agentic capabilities are not an incidental extension of scaling model size; rather, they arise from the architectural synthesis of large-scale neural representations with structured cognitive components—encompassing persistent memory hierarchies, interleaved reasoning-acting frameworks, dynamic tool integration, and multi-agent coordination protocols. By establishing a dual-paradigm taxonomy, this work has decoupled the historical contributions of symbolic architectures from the stochastic mechanics of neural orchestration, resolving the pervasive challenge of conceptual retrofitting. Empirical case studies across automated software engineering, 6G communication networks, healthcare, and physical robotics confirm that multi-agent systems can achieve remarkable operational efficiencies, yet they simultaneously introduce severe vulnerabilities spanning prompt injection, memory poisoning, coordination failures, and the lethal trifecta of unauthorized tool use. Addressing these systemic risks demands an equally sophisticated assurance stack: transitioning from outcome-based benchmarking to rigorous process-level evaluation, implementing defense-in-depth security across the agent lifecycle, and establishing multi-tier release gating. Moving forward, realizing the full potential of Agentic AI will depend on our ability to navigate the tension between autonomy and controllability. By embracing neuro-symbolic convergence, green computing economics, and robust socio-technical governance, the scientific community can guide the evolution of Agentic AI toward safe, auditable, and beneficial intelligence that fundamentally augments human capability across the global technological ecosystem.

References

[1] M. R. Morris, J. Sohl-Dickstein, N. Fiedel, T. Warkentin, A. Dafoe, A. Faust, C. Farabet, and S. Legg, "Position: Levels of AGI for operationalizing progress on the path to AGI," in Proc. 41st Int. Conf. Mach. Learn. (ICML), Vienna, Austria, 2024. [2] R. Burnell, Y. Yamamori, O. Firat, K. Olszewska, S. Hughes-Fitt, O. Kelly, I. R. Galatzer-Levy, M. R. Morris, A. Dafoe, A. M. Snyder, N. D. Goodman, M. Botvinick, and S. Legg, "Measuring progress toward AGI: A cognitive framework," arXiv preprint arXiv:2605.28405, 2026. [3] H. Derouiche, H. Mezni, and Z. Brahmi, "Agentic AI frameworks: Architectures, protocols, and design challenges," arXiv preprint arXiv:2508.10146, 2025. [4] J. Qi, M. Li, J. Liu, Y. Shu, D. Yu, S. Ma, W. Cui, Y. Zhao, Y. Chen, R. Jiang, I. King, and Z. Xu, "Towards trustworthy agentic AI: A comprehensive survey of safety, robustness, privacy, and system security," Academia AI and Applications, vol. 2, 2026. [5] S. T. Erukude, S. R. Veluru, and V. C. Marella, "Agentic AI - The rise of autonomous intelligent agents in the era of LLMs," Indian J. Comput. Sci. Eng. (IJCSE), vol. 16, no. 1, pp. 9–16, 2025. [6] R. Sapkota, K. I. Roumeliotis, and M. Karkee, "Vibe coding vs. agentic coding: Fundamentals and practical implications of agentic AI," arXiv preprint arXiv:2505.19443, 2025. [7] S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, "ReAct: Synergizing reasoning and acting in language models," in Proc. 11th Int. Conf. Learn. Represent. (ICLR), Kigali, Rwanda, 2023. [8] Q. Wu, G. Bansal, J. Zhang, Y. Wu, B. Li, E. Zhu, L. Jiang, X. Zhang, S. Zhang, J. Liu, A. H. Awadallah, R. W. White, D. Burger, and C. Wang, "AutoGen: Enabling next-gen LLM applications via multi-agent conversation," arXiv preprint arXiv:2308.08155, 2023. [9] P. K. Donta, A. Saleh, Y. Li, S. Vaishnav, K. Fang, H. Feng, Y. Xia, T. R. Gadekallu, Q. Zhang, X. Shi, A. Beikmohammadi, S. Magnússon, I. Murturi, C. K. Dehury, M. Paprzycki, L. Lovén, S. Tarkoma, and S. Dustdar, "Socio-technical aspects of agentic AI," arXiv preprint arXiv:2601.06064, 2025. [10] X. Meng, P. Gao, Z. Ma, and C. Peng, "LLM-based agents for automated bug fixing: How far are we?," arXiv preprint arXiv:2411.10213, 2024.

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Publication Date

9/9/2026

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