AI Agents: Evolution, Architecture, and Real-World Applications
Naveen Krishnan
TL;DR
The paper traces the ascent of AI agents from rule-based systems to autonomous, LLM-enhanced architectures that integrate perception, memory, planning, tool use, and environment interaction. It offers a unified architectural framework and critiques current evaluation practices, proposing a holistic four-dimensional framework (capability, efficiency, robustness, safety) with real-world applicability and reproducibility considerations. Real-world deployments span enterprise, personal productivity, and specialized domains, with documented gains in efficiency, decision support, and automation, while illustrating persistent challenges in reasoning, memory management, tool integration, and ethics. The work highlights technical hurdles, ethical risks, and long-term transformative visions, urging continued multidisciplinary research, robust governance, and human-centered collaboration to realize beneficial, scalable agent systems.
Abstract
This paper examines the evolution, architecture, and practical applications of AI agents from their early, rule-based incarnations to modern sophisticated systems that integrate large language models with dedicated modules for perception, planning, and tool use. Emphasizing both theoretical foundations and real-world deployments, the paper reviews key agent paradigms, discusses limitations of current evaluation benchmarks, and proposes a holistic evaluation framework that balances task effectiveness, efficiency, robustness, and safety. Applications across enterprise, personal assistance, and specialized domains are analyzed, with insights into future research directions for more resilient and adaptive AI agent systems.