Building Scalable and Reliable Agentic AI Systems: A Technical Blueprint for Autonomous Intelligence
DOI:
https://doi.org/10.65150/EP-gjetr/V1E3/2025-07Keywords:
Activated carbon, activated carbon/Fe₃O₄, wastewater treatment, Cu removal, coconut shellAbstract
Agentic AI systems represent a significant evolution beyond traditional reactive models by enabling autonomous, goal-directed behavior that integrates perception, planning, decision-making, tool use, and continuous learning. This paper synthesizes research and industry advances from 2022–2025 to outline the foundational design principles, technical architectures, and real-world case studies that characterize effective agentic AI. We highlight core principles such as outcome-aligned goal formulation, governed autonomy through guardrails and constraints, human oversight and accountability, transparency and auditability, iterative self-improvement, and modular system design. These principles are mapped to concrete architectural components including memory and knowledge systems, planning and reasoning modules, tool-use interfaces, reflection and feedback mechanisms, and multi-agent orchestration frameworks. Through an analysis of key systems—AutoGPT, CICERO, Generative Agents, and Voyager—we illustrate how these components enable long-horizon reasoning, strategic negotiation, lifelike social behavior, and autonomous skill acquisition in open-ended environments. The review emphasizes both the opportunities and the reliability, safety, and alignment challenges inherent in deploying autonomous agents. By integrating interdisciplinary insights across AI engineering, cognitive architecture, and responsible AI governance, this paper provides a roadmap for designing agentic systems that are effective, adaptive, and safe for real-world applications.
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Copyright (c) 2025 Dr. Sanjay Nakharu Prasad Kumar (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
