THE ARCHITECTURE OF AGENTIC AI SYSTEMS: A TECHNICAL STUDY ON PLANNING, MEMORY, GOAL-ORIENTED REASONING, AND ENVIRONMENT INTERACTION FOR BUILDING ROBUST AUTONOMOUS AGENTS
DOI:
https://doi.org/10.29121/9shxnt36Keywords:
Agentic AI, Autonomous Agents, Planning Algorithms, Memory Architectures, Goal-Oriented Reasoning, Environment Interaction, Multi-Agent Systems, AI BenchmarksAbstract
This study delves into the architectural foundations of agentic AI systems, emphasising planning mechanisms, memory structures, goal-oriented reasoning processes, and environment interaction protocols essential for developing robust autonomous agents. Employing a mixed-methods approach, including a systematic literature review of 10 key studies and empirical analysis using benchmarks like τ-Bench and Auto-SLURP, the research uncovers critical design principles that enhance agent autonomy and reliability. Key findings reveal that hybrid memory architectures integrating episodic and vector-based storage improve long-horizon planning by 28% in simulated environments, while multi-agent orchestration frameworks mitigate reasoning errors in dynamic interactions. These insights underscore the need for scalable, ethical architectures to bridge current gaps in real-world deployment. Ultimately, the study contributes a reproducible framework for agent design, advocating for interdisciplinary integration to advance AI toward general intelligence, with implications for industries like healthcare and logistics.
References
Arora, P., and Bhardwaj, S. (2024). Mitigating the Security Issues and Challenges in the Internet of Things (IoT) Framework for Enhanced Security. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 7(7).
Arora, P., and Bhardwaj, S. (2024). Research on Various Security Techniques for Data Protection in Cloud Computing with Cryptography Structures. International Journal of Innovative Research in Computer and Communication Engineering, 12(1).
Chen, L., Wang, T., Xiao, J., and Li, B. (2024). Persistent Long-Term Memory for Continual Learning in Agentic AI Systems. ACM Transactions on Intelligent Systems and Technology, 15(1), 1–24. https://doi.org/10.1145/nnnnnnn
Kumar, V. A., Bhardwaj, S., and Lather, M. (2024). Cybersecurity and Safeguarding Digital Assets: An Analysis of Regulatory Frameworks, Legal Liability and Enforcement Mechanisms. Productivity, 65(1).
LangChain. (2024). State of AI Agents Report. LangChain.
Park, S., et al. (2024). Benchmarking Agentic AI Frameworks. Proceedings of NeurIPS 2024.
Rahman, F., Ahmed, S., and Khan, M. (2024). Scalability Analysis of Modular Memory Architectures in Large-Scale Agentic AI Systems. Future Generation Computer Systems, 151, 350–362. https://doi.org/10.1016/j.future.2023.09.021
Sharma, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
Sharma, S. (2023). Homomorphic Encryption: Enabling Secure Cloud Data Processing.
Sharma, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
Sharma, S. (2025). A Cloud-Centric Approach to Real-Time Product Recommendations in E-Commerce Platforms. Journal of Science Technology and Research, 6(1), 1–11.
Tambi, V. K. (2024). Cloud-Native Model Deployment for Financial Applications. International Journal of Current Engineering and Scientific Research (IJCESR), 11(2), 36–45.
Tambi, V. K. (2024). Enhanced Kubernetes Monitoring Through Distributed Event Processing. International Journal of Research in Electronics and Computer Engineering, 12(3), 1–16.
Tambi, V. K. (2025). Scalable Kubernetes Workload Orchestration for Multi-Cloud Environments. The Research Journal (TRJ): A Unit of I2OR, 11(1), 1–6.
Tambi, V. K., and Singh, N. (2023). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence That Is Generative. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 12(10).
Tambi, V. K., and Singh, N. (2023). Evaluation of Web Services Using Various Metrics for Mobile Environments and Multimedia Conferences Based on SOAP and REST Principles. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 6(2).
Tambi, V. K., and Singh, N. (2024). A Comparison of SQL and No-SQL Database Management Systems for Unstructured Data. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 13(7).
Tambi, V. K., and Singh, N. (2024). A Comprehensive Empirical Study Determining Practitioners' Views on Docker Development Difficulties: Stack Overflow Analysis. International Journal of Innovative Research in Computer and Communication Engineering, 12(1).
Wang, R., Liu, X., and Zhou, Z. (2023). Episodic Memory Mechanisms for Experience Reuse in Reinforcement Learning Agents. Machine Learning, 112(9), 3511–3534. https://doi.org/10.1007/s10994-023-06310-4
Published
Issue
Section
License
Copyright (c) 2026 Ajay Simha Rangappa (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
