Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions
Agentic AI represents a paradigm shift from passive AI systems to autonomous agents capable of goal-directed reasoning, planning, and execution across diverse domains The paper provides a comprehensive taxonomy covering the historical evolution, architectural patterns, working principles, and functional capabilities of agentic AI systems A novel stakeholder adoption framework is proposed, integrating system quality dimensions to explain and predict intention to use agentic AI technologies Key re
Analysis
TL;DR
- Agentic AI represents a paradigm shift from passive AI systems to autonomous agents capable of goal-directed reasoning, planning, and execution across diverse domains
- The paper provides a comprehensive taxonomy covering the historical evolution, architectural patterns, working principles, and functional capabilities of agentic AI systems
- A novel stakeholder adoption framework is proposed, integrating system quality dimensions to explain and predict intention to use agentic AI technologies
- Key research gaps are identified around reliability, safety, interpretability, and the need for standardized evaluation benchmarks in the agentic AI space
- The review maps real-world applications spanning healthcare, finance, education, robotics, and enterprise automation, highlighting transformational potential
Why It Matters
This review synthesizes the rapidly evolving agentic AI landscape into a structured knowledge base, making it essential reading for researchers and practitioners seeking to understand both the theoretical foundations and practical deployment considerations of autonomous AI systems. The proposed adoption framework offers a novel lens for organizations evaluating agentic AI integration, bridging the gap between technical capability and organizational decision-making.
Technical Details
- The paper traces the theoretical evolution of agency in artificial systems, examining how agentic AI moves beyond reactive pattern matching toward proactive goal-oriented behavior with planning, memory, and tool-use capabilities
- Architectural patterns discussed include single-agent and multi-agent systems, with emphasis on components such as perception modules, reasoning engines, action selectors, and feedback loops that enable autonomous operation
- Real-world applications are catalogued across domains including healthcare (diagnostic agents, treatment planning), finance (algorithmic trading, risk assessment), education (personalized tutoring), and enterprise (workflow automation)
- The proposed adoption framework integrates system quality attributes—such as reliability, responsiveness, and adaptability—with stakeholder intention models to predict and explain organizational adoption behavior
- Current challenges identified include trust and transparency deficits, safety and alignment concerns, evaluation standardization gaps, and the need for robust long-horizon task execution capabilities
Industry Insight
- Organizations should prioritize building internal agentic AI literacy and establishing governance frameworks before large-scale deployment, as the adoption challenges extend well beyond technical feasibility into trust and change management
- The lack of standardized benchmarks for agentic AI evaluation represents a significant market risk; early movers who develop or adopt rigorous evaluation protocols will gain a competitive advantage in proving system reliability
- Multi-agent architectures are likely to dominate enterprise deployments, as complex business workflows benefit from specialized agents collaborating rather than monolithic systems attempting end-to-end autonomy
Disclaimer: The above content is generated by AI and is for reference only.