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Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions

AKM Bahalul Haque, Al Amin Islam Ridoy, Mohammad Rayhan, Ivan Porres

arXiv:2608.18110v1cs.AI

TL;DR

The paper addresses the need for a comprehensive understanding of Agentic AI and its research directions. It conducts a systematic review of recent literature, synthesizing the technology’s evolution, architecture, applications, adoption factors, challenges, and future directions. The review provides a synthesized account of current developments and proposes a stakeholder adoption framework based on system quality dimensions.

  • Problem

    Existing research requires a comprehensive examination of Agentic AI’s architecture, working principles, applications, research deficiencies, and future directions.

  • Method

    The paper conducts a systematic literature review and synthesizes research on Agentic AI’s evolution, architecture, workflow, applications, adoption factors, challenges, and future directions.

  • Results

    The review uses 94 articles and reports current multidisciplinary Agentic AI findings, adoption factors, implementation scenarios, challenges, and future research directions.

  • Takeaways & Limitations

    The review offers researchers and practitioners a comprehensive understanding of Agentic AI’s current developments and applications while proposing a stakeholder adoption framework.

  • Takeaways & Limitations

    The search excluded publisher-specific libraries and preprint servers, and the proposed architecture was not validated in this work.

Abstract

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Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapid transformation across various domains.This rapid advancement and the potential to revolutionize various domains advocate the need for a deeper understanding and firm grasp of the technology. Moreover, an investigation into state of the art research directions in agentic AI needs to be conducted to comprehensively assess the potential scope for improvement and application.Therefore, to address these objectives, a comprehensive review can provide researchers and practitioners with valuable insights into the current state and future research scopes of agentic AI.Hence, this work considers the recently published scholarly contributions in agentic AI across various domains and discusses the fundamentals and working principles of Agentic AI, traces the historical and theoretical evolution of agency in artificial systems, explores and discusses Agentic AIs architecture, working principles, and functionalities, explores real-world applications of Agentic AI across various domains, analyzes the research findings, identifies current challenges, and discuss potential future research directions, and proposes a comprehensive framework of stakeholders intention to use and adopt Agentic AI with the help of proposed system quality dimensions.Therefore, this systematic review provides researchers and practitioners with a comprehensive understanding of Agentic AI, its current developments and applications, highlights key research gaps, and outlines future research directions.

1 Introduction

This review examines Agentic AI as autonomous, adaptable, goal-oriented systems and synthesizes its evolution, architecture, applications, adoption factors, challenges, and future research directions.

  • Foundations: Agentic AI systems are autonomous, independent, and goal-oriented, reacting to inputs while perceiving environmental contexts that guide their actions.
  • Evolution: Agentic AI has evolved from rule-based systems toward hierarchical, multi-agent, and collaborative frameworks that divide complex tasks into subtasks requiring minimal human interaction.
  • Applications and challenges: Agentic AI research spans healthcare, customer support, banking, e-commerce, social media, and transportation, while raising concerns about computational costs, transparency, fairness, privacy, security, and ethics.
  • Review objectives: The review traces agency’s evolution, analyzes Agentic AI architecture and functions, examines applications, identifies adoption factors, and discusses research gaps and future directions.
  • Review objectives: The paper proposes a comprehensive framework for stakeholders’ intention to use and adopt Agentic AI using proposed system quality dimensions.

2 Research Strategy

The study uses a systematic literature review to identify and assess recent Agentic AI research from Scopus and Web of Science, supplemented by snowball sampling. The final corpus contains 94 articles.

  • Research design: The review follows a systematic literature review technique for software engineering to investigate Agentic AI’s evolution, background, and working principles.
  • Search strategy: Searches used Agentic AI-related and adoption, application, system quality, and working-principle terms across article titles, abstracts, and keywords.
  • Screening and quality assessment: Scopus and Web of Science were selected for their comprehensive scholarly indexing, followed by inclusion, exclusion, and structured quality-assessment procedures.
  • Eligibility criteria: The review included English-language journal articles and conference proceedings published from 2021 to 2025, covering conceptual or experimental implementations.
  • Study selection: 94 articles were used: 2,608 records underwent title-and-abstract screening, 593 advanced, 72 remained after full-text screening, and 22 were added through snowball sampling.

3 Evolutionary Background of Agentic AI

Agentic AI evolved from rigid, rule-based systems toward proactive, adaptive, and interconnected cognitive ecosystems. Its defining progression combines autonomy, goal-directed planning, multimodal perception, multi-agent coordination, and continuous learning to support complex task execution.

  • Evolution of AI: AI evolved from hand-coded symbolic systems toward interconnected cognitive ecosystems capable of autonomous reasoning, planning, and complex task execution.The trajectory described spans rule-based symbolic AI, later generative systems, and contemporary AI agents augmented through LLM-API frameworks.
  • Defining characteristics: Agentic AI perceives multimodal environments, forms objectives through reasoning and planning, acts independently, learns continuously, and coordinates with other agents.The paper frames these capabilities as the operational characteristics distinguishing Agentic AI within a distributed, goal-driven ecosystem.
  • Defining characteristics: Autonomy enables independent strategy, resource selection, action, performance assessment, error detection, and exception handling with minimal human involvement.The system is described as continuously assessing its performance and autonomously addressing issues.
  • Defining characteristics: Agentic AI decomposes complex goals into smaller steps, interprets multimodal input, coordinates specialized agents, and adapts through feedback and accumulated experience.These capabilities combine goal decomposition, environmental interaction, structured collaboration, and learning under changing conditions.
  • Comparison among AI paradigms: Traditional AI is suited to static rule-based tasks, whereas Generative AI primarily creates one-shot outputs without persistent memory, tool interaction, self-correction, or long-horizon planning.The comparison identifies limitations that separate reactive or content-generating paradigms from systems designed for extended autonomous task execution.
  • Comparison among AI paradigms: Agentic AI addresses these gaps through a closed loop of perceiving, planning, acting, observing, reflecting, and repeating for self-correction and verifiable execution.The closed-loop architecture is presented as a bridge between producing information and taking reliable action on complex tasks.
  • Comparison among AI paradigms: AI agents can execute tasks independently but may lack lateral delegation awareness, while uncoordinated multi-agent systems may duplicate processing and conflict.The paper uses these distinctions to motivate Agentic AI’s broader coordination and orchestration capabilities.

4 Architecture and Workflow of Agentic AI

Agentic AI uses a layered, cognitively distributed architecture combining multimodal interfaces, LLM reasoning, structured memory, orchestration, multi-agent execution, and adaptive feedback. Its workflow also includes security validation to filter malicious or invalid inputs before task processing.

  • Architectural framework: The architecture combines multimodal interfaces, LLM-based cognition, layered knowledge, centralized orchestration, distributed agents, and adaptive response synthesis.The interface handles text, speech, vision, and sensor streams; LLMs interpret and reason; the knowledge base supplies context; and the orchestrator coordinates agents.
  • LLM cognition: LLMs transform user inputs into abstract goals, then into cognitive workflows through action planning, multi-hop reasoning, and plan adjustment.The system evaluates hypothetical action consequences, incorporates previous experience, and uses methods such as Chain-of-Thought prompting and self-reflective decoding.
  • Knowledge integration: Retrieval-augmented mechanisms and external databases, APIs, or knowledge graphs maintain contextual continuity, personalize responses, and support access to current information.The system can use stored user profiles, external repositories, and cross-referenced information sources across interactions.
  • Multilayered knowledge system: The knowledge system separates persistent, session-oriented, and mission-specific information according to lifespan, relevance, and contextual specificity.Long-term memory stores stable rules and ontologies, short-term memory captures current interaction context, and task-specific memory tracks execution flows, variables, delegation, and intermediate outputs.
  • Workflow coordination: The orchestrator decomposes directives, assigns tasks, monitors progress, synthesizes responses, and maintains adaptation through feedback across the six-stage workflow.The task manager distributes objectives to appropriate agents, while the orchestrator coordinates execution and response aggregation.
  • Security validation: Security validation uses zero-trust architectures, threat detection, verification, access control, and policy checking to filter illogical, invalid, or malicious inputs before task management.These controls address risks including increased computational cost, system crashes, and outputs that may violate ethics or privacy.

Long-Term Memory

Long-term memory stores persistent operational knowledge and supports contextual continuity, personalization, learning, and adaptation across the orchestrator workflow. It complements working memory, which tracks current execution context and intermediate results.

  • Long-term memory stores user preferences, task patterns, agent performance, decomposition templates, and domain-specific knowledge graphs.
  • It transforms the orchestrator from a reactive system into an adaptive system that learns and evolves from operational experience.
  • The orchestrator decomposes goals, coordinates agents, monitors progress, synthesizes responses, and adapts through feedback.
  • Hierarchical coordination provides clear accountability, centralized oversight, and layer-based scalability, but orchestrator or higher-level failures can disable affected portions of the system.

Merit:

The reviewed coordination and monitoring designs offer distinct advantages, including simplicity, scalability, resilience, adaptability, and measurable system optimization. Their trade-offs include inflexibility, bottlenecks, resource demands, and architectural complexity.

  • Sequential coordination provides simplicity, traceability, and expertise-based processing, but prevents parallelism and can collapse when one agent fails.
  • Peer-to-peer coordination supports resilience, local scalability, and adaptability through self-organization, while making emergent behavior and fault identification harder to predict.
  • Centralized orchestration simplifies management and agent scaling and enables collaborative specialization, but can overload the orchestrator and limit direct agent collaboration.
  • Hybrid orchestration combines centralized oversight with peer interaction to improve resilience, flexibility, coordination efficiency, and resource sharing.
  • Hybrid designs still face orchestrator bottlenecks, dual communication-protocol complexity, and difficult autonomy-oversight trade-offs.
  • System monitoring and evolution provide telemetry analysis, resource and conflict management, error handling, learning-based adaptation, and governance enforcement.

Agent Interaction-Level Metrics

Agent interaction-level evaluation extends beyond isolated benchmark scores to assess communication, coordination, resource use, resilience, and user-facing performance. The paper also identifies deployment barriers spanning architecture, computation, ethics, and governance.

  • Interaction metrics measure communication delay, conflict reconciliation, task handover, consensus convergence, information sharing, and load balancing.
  • System-level metrics include end-to-end latency, aggregate throughput, resource saturation, SLA compliance, availability, resilience, synchronization, and user satisfaction.
  • Agentic evaluation tests end-to-end completion, error recovery, state maintenance, and environmental observation rather than isolated capabilities alone.
  • AgentBench, WebArena, SWE-bench, and GAIA evaluate interactive environments, long-horizon web tasks, software engineering problems, and multimodal tool-based reasoning.
  • Resource monitoring anticipates demand spikes and enables dynamic allocation based on immediate needs, predicted requirements, priorities, and system-wide objectives.
  • Deployment is constrained by integration friction, long-horizon coordination difficulties, high resource consumption and latency, privacy risks, accountability gaps, bias, and regulatory uncertainty.

5 Applications of Agentic AI

The review describes Agentic AI applications across HCI, healthcare, education, customer service, and network infrastructure, emphasizing personalization, autonomy, continuous adaptation, and multi-agent coordination. It also identifies deployment constraints involving transparency, privacy, cybersecurity, integration, latency, and computational resources.

  • Human-Computer Interaction (HCI): Agentic AI supports personalized, context-aware human-computer interaction through memory retention, iterative feedback, goal-driven actions, and multi-step planning.These capabilities improve trust and engagement in structured interaction environments, although persistent user models and long-term contextual memory remain challenging.
  • Healthcare: In healthcare, Agentic AI enables continuous patient monitoring, adaptive support, and multi-step workflows spanning diagnosis, treatment planning, and intervention.Conversational agents also support mental-health interactions through personalized and context-aware responses.
  • Education: Educational agents function as individual learning assistants or collaborative learning agents, providing personalized tutoring, adaptive feedback, coordination, and shared problem-solving.Reported benefits include reduced cognitive load, improved conceptual understanding, increased engagement, and stronger performance or self-efficacy.
  • Education: Education-sector adoption is constrained by students’ over-reliance on AI, alongside concerns about teacher roles, privacy, equal access, and maintaining long-term student models.The review argues that agentic systems should complement rather than replace human instruction.
  • Customer Service and Decision Support: Customer-service and decision-support systems use Agentic AI for process optimization, while failures can damage customer loyalty more than failures of IVR systems.The effect is especially pronounced among highly involved consumers who expect intelligent behavior.
  • Network and Cyber-Infrastructure: Network and cyber-infrastructure applications include autonomous diagnostics, optimization, threat response, monitoring, anomaly detection, resource scheduling, and multi-agent coordination.These systems can improve fault detection, accuracy, efficiency, scalability, and autonomous network management, but autonomy expands cybersecurity, interoperability, governance, latency, and resource challenges.

6 Adoption Factors of Agentic AI Usage

The review frames adoption factors as system quality dimensions that shape stakeholders’ intentions to use Agentic AI. These dimensions include faster decision-making, trust and reliance, resource optimization, service quality, productivity, scalability, and data handling.

  • Adoption Factors: The proposed adoption factors are also treated as system quality dimensions that can influence stakeholders’ intentions to adopt and use Agentic AI.The framework is grounded in technology attributes that affect behavioral beliefs and intentions in personal or industrial settings.
  • Decision-Making: Simplified and faster decision-making is an adoption factor because autonomous agents can support complex tasks and accelerate collective decisions.The review links enhanced decision-making with optimization and increased resilience in system components.
  • Trust and Reliance: Trust and reliance develop as agents learn from user interaction, adapt to context, and provide proactive, responsive support.The review also notes that moral identity can influence unethical behavior in autonomous consumer decision-making.
  • Resource Optimization: Autonomous resource allocation, scheduling, and conflict resolution can improve operational efficiency and quality of service.In software-defined networking, agents dynamically schedule resources according to changing network needs.
  • Service Provision: Agentic AI can provide continuous customer service, personalized recommendations, real-time information, and enhanced user engagement.Its autonomous operation also supports communication management and emotional-companionship experiences.
  • Productivity: Autonomy, separate task responsibilities, and optimized resource planning can reduce human intervention and increase productivity across domains.Learning from past experience is described as supporting scientific discovery and innovation.
  • Scalability and Data Handling: Modular Agentic AI systems can scale to large data volumes and support archival analysis, comparative analysis, strategy recommendation, and diverse analytics.These capabilities contribute to scalability and broader data-handling versatility.

7 Synthesized Framework of Agentic AI Development and Adoption

The paper proposes a synthesized framework connecting Agentic AI system-quality attributes with stakeholders’ adoption and use intentions. It combines object-based attitudes, behavioral beliefs, and possible direct or mediated relationships between technology attributes and behavioral beliefs.

  • Framework Components: The synthesized framework organizes Agentic AI system-quality attributes such as faster decisions, reliance, resource optimization, operational efficiency, scalability, data handling, and seamless service provision.The framework is presented as a basis for Agentic AI research and adoption.
  • Theoretical Relationships: The framework draws on Wixom and Todd’s theory by treating technology attributes as object-based beliefs and their effects as behavioral beliefs mediated through object-based attitudes.It also acknowledges empirical evidence that object-based beliefs may directly affect behavioral beliefs without mediation.

8 Critical Analysis of Future Research Agendas

The review uses problematization to move beyond gap-spotting and identify critical research agendas for Agentic AI. Priorities include human-AI collaboration, domain validation, ethical governance, stronger data and methods, technical scalability, and integration with emerging technologies.

  • Research Approach: Problematization is used to identify limitations and challenges in existing literature and formulate research questions about how Agentic AI can be improved.The review notes that many applications remain theoretical or have not been implemented at large scale.
  • Ethical and Behavioral Considerations: Ethical research should examine transparency, accountability, trust, explainability, unethical behavior, hermeneutic harm, emotional responses, and regulation.The agenda focuses on complex and long-term human-agent interactions.
  • Data and Methodological Gaps: Data and methodological priorities include more diverse datasets, robust field studies, longitudinal evaluations, organized domain knowledge, and human-baseline comparisons.These methods are intended to examine data imbalance, generalizability, controlled-versus-real-world performance, and long-term applicability.
  • Technical and Computational Gaps: Technical research should develop real-time learning systems and computationally efficient, scalable architectures for settings such as 6G networks and robotics.Further priorities include reward-function parameterization and explainability.
  • Regulatory and Governance: Governance research should establish security inspections, standardized communication protocols, industry-specific adoption strategies, regulatory-compliant frameworks, and healthcare clinical trials.These measures address vulnerabilities, adoption resistance, and domain-specific standards.
  • Human-AI Collaboration and Cognitive Bias: Future work should develop human-agent collaboration frameworks, study cognitive bias, and use digital twins to simulate interactive collaboration across domains.Suggested application areas include transportation, healthcare, aviation, and construction.
  • Domain-Specific Research: Research should validate Agentic AI through experiments, case studies, and pilot projects in underexplored domains while defining domain-specific requirements and metrics.Proposed domains include transportation, healthcare, aerospace, finance, and social media.
  • Integration with Emerging Technologies: Researchers should evaluate integration between Agentic AI and technologies including IoT, blockchain, wearable sensors, quantum computing, and intelligent systems.Suggested approaches include qualitative, quantitative, and design-thinking research for adoption strategy development.

9 Contribution to Research and Practice

The review contributes a broad account of Agentic AI by synthesizing its history, architecture, applications, adoption factors, and future research directions for researchers and practitioners.

  • Historical perspectives: The review provides a comprehensive historical perspective and distinguishes Agentic AI from other AI paradigms.It is presented as a resource covering both foundational concepts and recent advances.
  • Architecture and principles: It explains Agentic AI principles, components, architecture, and workflow in detail for readers with basic AI knowledge.The architecture and workflow are illustrated to improve accessibility.
  • Applications: The review surveys empirical and proposed real-world applications across various domains.This allows readers to see both implemented applications and potential application areas.
  • Adoption factors: It analyzes potential adoption factors across disciplines to inform feasibility studies and identify opportunities for improvement.The factors are also presented as indicators and motivations for real-world application design.
  • Future research: The review evaluates findings, applications, and research gaps, then organizes future research avenues into domain-specific and application-specific directions.It also uses these directions to identify tentative future research questions.

10 Limitations

The study’s limitations concern its literature-search coverage and the lack of validation for its proposed architecture.

  • Search scope: The systematic review searched Scopus and Web of Science but excluded publisher-specific libraries and preprint servers such as arXiv.This limits the sources used to identify scholarly publications.
  • Architecture validation: The proposed architecture in Figure 7 was not validated in this work.The authors identify validation as a potential direction for future Agentic AI research.

11 Conclusion

The conclusion presents Agentic AI as an emerging technology requiring applicability and feasibility investigation, while highlighting multidisciplinary applications, adoption factors, and future research directions. It describes the technology as potentially useful for automating complex tasks and expanding service-providing applications.

  • Conclusion: Agentic AI requires investigation of its applicability and feasibility because of its emerging nature.The review examines multidisciplinary implementation and factors relevant to adoption.
  • Conclusion: The review identifies adoption factors that practitioners can consider before adopting Agentic AI.It also presents these factors as an opportunity for a more opportunistic perspective on implementation.
  • Conclusion: The research discusses future directions considered useful for both practitioners and researchers.These directions accompany the implementation scenarios examined in the review.
  • Conclusion: Agentic AI has significant potential for automating complex tasks and opening applications across service-providing scenarios.The conclusion characterizes research on Agentic AI as timely and necessary for advancing AI.
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