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

1 Introduction

• Agentic AI is autonomous, independent, and goal-oriented, using contextual perception, reasoning, and proactive or reactive actions rather than fixed-term, task-specific, human-in-the-loop operation.Source: §1 · p. 1
• Agentic AI has evolved toward hierarchical, multi-agent, collaborative frameworks that decompose complex tasks into subtasks requiring minimal human interaction.Its applications span healthcare, finance, education, IoT, software engineering, and business process management.Source: §1 · p. 2
• The review responds to the absence of a comprehensive account covering Agentic AI’s background, working principles, applications, benefits, research gaps, and future potential.Existing work identifies concerns involving computational cost, transparency, fairness, privacy, security, ethics, data availability, and integration mismatches.Source: §1 · p. 2
• The paper traces agency’s evolution, analyzes Agentic AI’s layered architecture and functions, surveys real-world applications, identifies adoption factors as system-quality dimensions, and evaluates challenges and future research directions.Source: §1 · p. 2
• It proposes a comprehensive framework for stakeholders’ intention to use and adopt Agentic AI using the proposed system-quality dimensions.Source: §1 · p. 2

Motivation

• Next-generation AI must interpret high-level objectives, construct multi-stage plans, and execute operations independently because reactive architectures depend on human intervention and cannot autonomously pursue complex objectives.Agentic AI offers autonomy, reasoning, adaptability, planning, and action toward goal-directed behavior.Source: Motivation · p. 3

2 Research Strategy

• The study followed a software-engineering systematic literature-review technique, using a chronological search and selection process illustrated in Figure 1.Search terms included both Agentic AI and AI Agents, while screening excluded articles focused exclusively on AI Agents.Source: §2 · p. 3
• Scopus and Web of Science searches used a Boolean string covering Agentic AI concepts and adoption, application, system-quality, and working-principle topics.Keywords were searched in article titles, abstracts, and keywords, with searches limited to 2021–2025.Source: §2 · p. 3, Exclusion criteria · p. 4

Exclusion criteria

• Quality assessment evaluated methodological soundness, validity, and relevance, with authors independently assessing studies and resolving disagreements through discussion.Review articles, book chapters, editorials, other article types, and unavailable full-text articles were excluded.Source: §2 · pp. 3–4, Exclusion criteria · p. 4

Inclusion criteria

• Included studies were English-language journal or conference articles published from 2021 to 2025 that addressed conceptual or experimental implementations.These criteria defined the eligible publication types, time range, language, and research focus.Source: Inclusion criteria · p. 4, Exclusion criteria · p. 4

Exclusion criteria

• 94 articles were used after screening 4,130 database records, retaining 72 full-text studies and adding 22 articles through snowball sampling.The process screened 2,608 deduplicated records to 593 studies and then 72 full-text articles before snowball sampling expanded coverage of Agentic AI history, evolution, and architecture.Source: Exclusion criteria · p. 4

3 Evolutionary Background of Agentic AI

• Agentic AI emerged from symbolic AI, statistical learning, generative AI, and AI agents to address limited real-world interaction, autonomy, and independent decision-making.The trajectory moves from fixed rules and dataset-based pattern discovery to content generation, then toward interconnected systems capable of autonomous reasoning, planning, and complex task execution.Source: §3 · pp. 4, 7

3.1 Definition of Agentic AI and Key Aspects

• Agentic AI is a distributed, goal-driven ecosystem that perceives multimodal environments, plans and acts independently, learns continuously, and coordinates specialized agents with minimal human intervention.Its defining aspects are autonomy, goal-oriented behavior, perception and interaction, multi-agent coordination, and adaptability through experience or feedback.Source: §3.1 · p. 7

3.2 Expanded Comparison among AI Paradigms

• Compared with traditional and Generative AI, Agentic AI addresses long-horizon planning, dynamic execution, and minimal human intervention through a perceive → plan → act → observe → reflect → repeat architecture.This closed loop supports meta-cognition, self-correction, and verifiable execution of complex tasks, whereas Generative AI primarily produces one-shot outputs without memory, tool interaction, or self-evaluation.Source: §3.2 · pp. 7–8

3.3 Distinguishing Agentic AI from Closely Related Fields

• Unlike rule-based multi-agent systems, bounded autonomous systems, and reactive LLM-based agents, Agentic AI uses orchestration, layered memory, reflection, task decomposition, self-correction, and independent long-horizon objectives.It adapts to open-domain and cross-domain challenges rather than relying on hand-coded rules, narrow environments, constant prompting, or fixed communication protocols.Source: §3.3 · p. 8

4 Architecture and Workflow of Agentic AI; 4.1 System Overview

• The architecture combines multimodal processing, LLM cognition, structured knowledge, centralized orchestration, distributed agents, adaptive response synthesis, and continuous monitoring.This cognitively distributed design extends beyond monolithic models by linking specialized components into a self-optimizing ecosystem.Source: §4 · p. 9, §4.1 · pp. 10–11

4.1.1 User/System Interface

• The interface converts text, speech, vision, and sensor streams into standardized inputs while supporting transparent interaction, feedback-driven preferences, real-time or delayed processing, and access verification.Authorization and verification restrict access to intended users, while summarized processing steps improve transparency.Source: §4.1.1 · p. 11

4.1.2 Core Cognitive Engine: LLMs; Semantic Understanding and Abstraction:; Multi-Hop Reasoning and Planning; Personalization and Memory Recall:; Knowledge Integration and Justification:

• LLMs interpret multimodal inputs, infer intent, perform multi-step reasoning, generate goals, plan actions, integrate external knowledge, and justify conclusions through logical pathways or cited studies.Embeddings and attention preserve semantic relationships, while Chain-of-Thought prompting, retrieval augmentation, attention mechanisms, and embeddings support reasoning, personalization, and knowledge integration.Source: §4.1.2 · p. 11, Semantic Understanding and Abstraction · p. 11, Multi-Hop Reasoning and Planning · p. 11

4.1.3 Multilayered Knowledge System (L, S, TS); Long-Term Knowledge (L):; Short-Term Knowledge (S):; Task-Specific Knowledge (TS):

• The knowledge system separates persistent long-term facts, rapidly changing short-term session context, and frequently updated task-specific state to support efficient retrieval, execution, monitoring, and learning.Long-term stores contain curated rules and ontologies, short-term memory uses low-latency caches for current interactions, and task-specific memory tracks goals, runtime variables, delegation, tools, and intermediate outputs.Source: §4.1.3 · p. 12, Long-Term Knowledge (L) · p. 12, Short-Term Knowledge (S) · p. 13

4.2 Orchestrator: Central Coordination Layer; 4.2.1 Perception Layer; Input Interpretation:; Security Validation:; Task Standardization:; Task Decomposition:; Resource Orchestration:; Scheduling & Queueing:

• The orchestrator coordinates perception, task management, execution, response generation, memory, and feedback through a six-stage workflow that decomposes objectives, assigns agents, schedules dependencies, and synthesizes results.Task allocation considers agent capabilities, load, and service-level agreements [39], while dependency-aware scheduling and dynamic rescheduling reduce bottlenecks, latency, and stuck workflows.Source: §4.2 · p. 13, Input Interpretation · p. 14, Security Validation · p. 14

4.2.3 Execution Engine; Real-time Control:; Fault Handling:; Result Aggregation:

• The execution engine synchronizes distributed agents, reallocates resources, recovers from failures through retries or backup agents, and reconciles divergent outputs into reliable results.Confidence-weighted consensus and data fusion combine agent outputs, while checkpoint rollback and workflow reconfiguration address disruptions.Source: §4.2.3 · p. 15, Real-time Control · p. 15, Fault Handling · p. 15

4.2.4 Response Generator; Insight Synthesis:; User Adaptation:

• The response and adaptation layers transform aggregated results into secure, user-friendly deliverables and personalize tone using stored user profiles, preferences, and interaction histories.Outputs may be written summaries, charts, or actionable commands, while vector-store retrieval supports user-specific responses.Source: §4.2.4 · p. 15, Insight Synthesis · p. 16, User Adaptation · p. 16

4.2.5 Monitor & Feedback Mechanism; Metrics & Logging; Self-Healing Triggers; Learning Integration; 4.2.6 Memory Manager; Short-Term Memory

• Monitoring and memory management support continuous improvement by logging performance, triggering self-healing, integrating feedback and online learning, and consolidating working context into durable knowledge.The memory manager preserves conversational context, task graphs, agent metrics, intermediate results, feedback, and preferences, enabling fast synchronization and transfer of important short-term information to long-term memory.Source: §4.2.5 · p. 16, Metrics & Logging · p. 16, Self-Healing Triggers · p. 16

Long-Term Memory

• Persistent long-term memory stores interaction patterns, preferences, performance metrics, task-decomposition templates, and domain knowledge, enabling adaptation, personalization, and learning from past decisions.Without it, the orchestrator repeats mistakes, loses operational insights, and cannot personalize experiences.Source: Long-Term Memory · p. 17

Memory Access Patterns

• Microsecond memory access uses predictive loading, multi-level caching, historical access patterns, and advanced search to reduce latency and support real-time reasoning and coordination.Poor access patterns create bottlenecks, reduce user satisfaction, and limit complex reasoning.Source: Memory Access Patterns · p. 17

4.3.1 Agents’ Internal Structure

• Agents use a cyclical cognitive architecture linking goal setting, reasoning, planning, tool execution, criticism, and memory for verifiable objectives, informed actions, progress tracking, and adaptation.Goals encode objectives, constraints, and success thresholds; memory supplies prior plans and context, while the critic evaluates outcomes and feeds learning back into persistent knowledge.Source: §4.3.1 · pp. 17–18

4.3.2 Agent Interaction

• Agent interaction combines peer communication and orchestrator collaboration, enabling information exchange, collective decision-making, coordinated execution, task reassignment, and reinforcement-based strategy refinement.Agents exchange completion status, performance metrics, and causal traces, while peer collaboration supports capability matching, resource sharing, consensus, decomposition, and load balancing.Source: §4.3.2 · p. 19, Cross-Agent Collaboration · p. 19, Agent-Orchestrator Collaboration · p. 19

4.3.3 Coordination Topologies

• Coordination topologies determine agent connections, information flow, decision-making, scalability, resilience, and efficiency across the cognitive system.Figure 5 presents the possible orchestrator-agent coordination topologies.Source: §4.3.3 · p. 19, Figure 5 · p. 20

Hierarchical Orchestration (Tree Topology)

• Hierarchical orchestration initiates and decomposes goals through an orchestrator, delegates subtasks across manager and worker layers, and returns subordinate results through the chain of command.Its clear structure, centralized oversight, and layered scalability are offset by orchestrator failure risks and potential communication bottlenecks.Source: Hierarchical Orchestration (Tree… · p. 19, How it works · pp. 19–20, Merit · p. 20

Decentralized Coordination (Marketplace)

• Decentralized marketplace coordination posts tasks to a shared platform, lets agents negotiate and bid according to capabilities and costs, and shares results without explicit central control.It offers fault tolerance, scalability, and adaptability, but negotiation delays, coordination complexity, and inefficient allocation can arise.Source: Decentralized Coordination (Marketplace) · p. 20, How it works · pp. 20–21, Merit · p. 21

Chained Workflow

• Chained workflows process tasks sequentially from Agent1→Agent2→…→Agent N, with each agent applying focused expertise before the final agent delivers the result.The orchestrator defines the cyclic order, assigns the first task, and passes each output to the next agent until completion.Source: Chained Workflow · p. 21, How it works · p. 21

Merit:

• Sequential architectures offer simple fault identification, traceable progress, and expertise-based processing, but inflexibility, no parallelism, and single-agent failure limit performance.They suit data processing pipelines, software development, and manufacturing assembly where sequential workflows are needed.Source: Merit · p. 21, Demerit · p. 21

Swarm Intelligence (P2P Mesh)

• Swarm intelligence uses peer-to-peer neighbor interaction, simple rules, and emergent adaptation without continuous central coordination.It provides resilience, scalability, and adaptability, but introduces unpredictability, design complexity, and high resource demands.Source: Swarm Intelligence (P2P Mesh) · p. 22, How it works · p. 22, Merit · p. 22

Centralized Star Orchestration

• Centralized star orchestration assigns subtasks, aggregates agent results, and supervises decisions through the orchestrator as the sole communication nexus.This simplifies coordination, auditing, policy enforcement, and agent scaling, but creates bottlenecks and limits direct collaboration.Source: Centralized Star Orchestration · p. 22, How it works · p. 22, Merit · p. 22

Hybrid Orchestration with Peer-to-Peer

• Hybrid orchestration combines centralized oversight with peer interaction, enabling independent subtask execution, local collaboration, and orchestrator feedback for adaptation.It improves resilience, flexibility, and coordination efficiency while retaining risks from orchestrator overload, protocol complexity, and autonomy-oversight tradeoffs.Source: Hybrid Orchestration with Peer-to-Peer · p. 23, How it works · p. 23, Merit · p. 23

4.4 System Monitoring and Evolution Layer

• The system monitoring and evolution layer supports continuous telemetry analysis, conflict and resource management, error handling, learning-based adaptation, and governance across the system.It operates beneath the orchestrator and agent pool as the substrate for observational and adaptive intelligence.Source: §4.4 · p. 23

4.4.1 Performance Evaluation Metrics

• Performance evaluation uses hierarchical quantitative and qualitative assessment across individual agents, interactions, and system-wide operations for fine-grained optimization.The framework extends beyond simple scalar metrics to tune individual components and optimize the full system.Source: §4.4.1 · pp. 23–24

Agent-Level Metrics

• Agent-level metrics assess reasoning latency, planning throughput, execution success rate, resource usage, memory retrieval, and learning convergence.Together, these metrics measure processing speed, plan generation, valid tool use, computational consumption, information retrieval, and training efficiency.Source: Agent-Level Metrics · p. 24
• Agent-level metrics also evaluate decision quality, resilience to adaptation, and novel task adaptability against ground truth, changing environments, and unseen tasks.These measures capture accuracy and confidence, performance maintenance under variation, and generalization beyond previously encountered tasks.Source: Agent-Level Metrics · p. 24

Agent Interaction-Level Metrics

• Interaction-level evaluation measures inter-agent latency, conflict resolution, task-handover success, consensus convergence, information-sharing efficiency, and load-balance fairness.These metrics capture communication delay, reconciliation time, retry-free transfer, communication rounds to agreement, relevant-information yield, and prevention of distribution bottlenecks.Source: Agent Interaction-Level Metrics · p. 24

System-Wide Metrics

• System-wide evaluation covers end-to-end latency, aggregate throughput, resource saturation and scaling events, SLA compliance, availability, error rate, resilience, synchronization, and user satisfaction.These measures span goal completion time and rate, cluster utilization and queueing, target-time compliance, uptime and critical failures, adverse-condition performance, component coordination, and usability feedback.Source: System-Wide Metrics · p. 24

4.4.2 Agent Evaluation Benchmarks

• Agentic evaluation must extend beyond static benchmarks to end-to-end completion, error recovery, state maintenance, environmental observation, and interactive task performance across benchmarks such as AgentBench, WebArena, SWE-bench, and GAIA.Reported examples include 2,294 SWE-bench issues, where Claude 2 solved 1.96%, and GAIA, where GPT-4 scored around 15% versus humans’ 92% accuracy.Source: §4.4.2 · p. 25

Error classification; Resolution strategies; Post-mortem analysis

• Error handling classifies failures as agent-level, system-level, or critical, applies automated recovery, escalation, and adaptive resolution, and uses post-mortem root-cause analysis to update knowledge, policies, and playbooks.The tiers range from reasoning and communication failures to coordination and cascading failures, then safety, security, integrity, exhaustion, and governance failures.Source: Error classification · p. 26, Resolution strategies · p. 26

Reinforcement Learning (RL); Meta-Reinforcement Learning; Retrieval-Augmented Generation (RAG)

• Continuous learning integrates distributed reinforcement learning, meta-reinforcement learning, and retrieval-augmented generation to improve collective efficiency, rapid adaptation, and context-grounded decisions.RL uses global rewards and stored state-action-reward information; meta-learning minimizes adaptation data and retraining; RAG retrieves relevant external knowledge to reduce hallucination and support complex queries.Source: Reinforcement Learning (RL) · p. 27, Meta-Reinforcement Learning · p. 27, Retrieval-Augmented Generation (RAG) · p. 27

Policy Enforcement Engine; Safety Monitors; Rule Update Pipelines

• Governance and safety enforce permissions, privacy, content restrictions, runtime risk detection, and evolving rules through policy engines, safety monitors, and rule-update pipelines.Controls can block actions, escalate to human oversight, shut down systems, detect unsafe sequences and adversarial attacks, and propagate updates as regulations and threat patterns change.Source: Policy Enforcement Engine · p. 28, Safety Monitors · p. 28, Rule Update Pipelines · p. 28

4.5 Comparative Analysis with the Existing Frameworks

• The proposed architecture centralizes orchestration over decentralized agents, using layered memory and performance, conflict, resource, learning, error, governance, and safety components for context awareness, fault tolerance, and system-wide alignment.This design addresses context degradation in AutoGen, rigid task decomposition in LangGraph, limited shared knowledge in CrewAI, and plugin-centered integration in Semantic Kernel.Source: §4.5 · p. 29

4.6 Critical BoElenecks of Agentic AI

• Architectural barriers arise from integrating probabilistic agents with deterministic systems, long-horizon reasoning and fragile coordination, while computational bottlenecks make sustained autonomy slow and costly at scale.Large models require substantial GPU/TPU power, continuous planning-acting-observing loops, many tokens, and iterative LLM calls; ethical deficits add privacy, accountability, explainability, bias, and regulatory risks.Source: §4.6 · p. 29

5.1 Human-Computer Interaction (HCI)

• In HCI, Agentic AI improves trust, engagement, personalization, and adaptive interaction through memory retention, contextual reasoning, goal-driven action, and multi-step planning, especially in structured environments.Iterative feedback loops enable systems to retain context, anticipate user needs, and adjust interaction strategies, but persistent user models, computational demands, manipulation risks, and user-control requirements limit deployment.Source: §5.1 · p. 30

5.2 Healthcare

• Healthcare applications span personalized conversational support, diagnostics, multi-omic analysis, and autonomous surgery, with continuous adaptation and multi-step workflows supporting context-dependent clinical decisions.Conversational systems are easier to deploy and scale, whereas diagnostic and surgical systems require stronger validation, reliability, explainability, privacy, safety, and professional control.Source: §5.2 · p. 31

5.3 Education

• In education, individual and collaborative agents provide contextual scaffolding, adaptive feedback, and continuous support that reduce cognitive load while improving learning, engagement, performance, and self-efficacy.Hybrid GenAI scaffolding outperformed standalone GenAI and traditional scaffolding, but over-reliance, teacher-role redesign, privacy, equitable access, and long-term student modeling remain adoption challenges.Source: §5.3 · pp. 31–32

5.4 Consumer Services and E-Commerce

• Consumer-service agents personalize interactions and automate marketing, pricing, inventory, support, decision-making, and optimization, but trust depends on moderate anthropomorphism, credible emotional behavior, and reliable performance.Agent failures can damage loyalty more than IVR failures, while integration with legacy infrastructure, privacy, profiling, bias, excessive automation, and limited transparency constrain adoption.Source: §5.4 · p. 32

5.5 Network and Cyber-Infrastructure

• In network and cyber-infrastructure, Agentic AI automates monitoring, diagnostics, optimization, threat response, lifecycle management, scheduling, and distributed coordination, improving accuracy, efficiency, scalability, and resource utilization.Goal-directed agents observe network states and execute real-time actions, but attack-surface expansion, interoperability, coordination, latency, resource demands, unintended behavior, and governance require built-in security and constraints.Source: §5.5 · pp. 32–33

5.6 Structured Taxonomy Linking Agentic Architecture, Learning, and Application

• The taxonomy links each application domain’s operational constraints to suitable agent topologies and learning strategies, recommending centralized star orchestration with Meta-RL and layered memory for HCI.The framework contrasts topology and learning trade-offs, including P2P unpredictability, tree and hybrid response limitations, chained-workflow inflexibility, RL trial costs, and RAG’s behavioral limitations.Source: §5.6 · p. 33

6 Adoption Factors of Agentic AI Usage

• The proposed adoption framework treats identified Agentic AI attributes as system-quality dimensions that influence stakeholders’ behavioral beliefs and intentions to adopt and use the technology, building on [106].The framework applies to personal and industrial settings.Source: §6 · p. 34

6.1 Simplified and Faster Decision Making

• Agentic AI simplifies and accelerates complex decision-making through autonomous, collectively optimized agents, improving resilience and situationally aware goal selection.These benefits are discussed in disaster management, digital twins, and vehicular systems.Source: §6.1 · pp. 34–35

6.2 Trust and Reliance on the System

• Learning from interaction, adapting to context, and autonomous human interaction build user reliance, confidence, and trust, while moral identity can affect unethical agent behavior.Trust and reliance may develop over time, particularly when users have limited domain proficiency.Source: §6.2 · p. 35

6.3 Sustainability, Resource Optimization, and Operational Efficiency

• Autonomous resource scheduling, allocation, and conflict resolution optimize resources, improve operational efficiency and service quality, and support continuous context-aware infrastructure management.Applications include software-defined networking, buildings, network infrastructures, and other industries.Source: §6.3 · p. 35

6.4 Enhanced Human AI Interaction and Seamless Service Provider

• Agentic AI enables 24/7, personalized, real-time, and contextual services that improve engagement, communication, learning support, and customer interaction.These capabilities include reducing learners’ cognitive load and independently organizing conversational topics.Source: §6.4 · p. 35

6.5 Creativity, Innovation, and Increased Productivity

• Autonomous operation, reduced human intervention, experience-based learning, and distributed responsibilities accelerate scientific discovery, innovation, and productivity through optimized resource planning.Agents separately manage tasks and improve the effectiveness of task execution.Source: §6.5 · p. 35

6.6 Scalability and Handling Large Volume of Data

• Modular Agentic AI systems scale to large data volumes, enabling comparative analysis, strategy recommendation, solution reuse, and diverse analytics for environmental monitoring.Examples include analyzing waste-management, food-consumption, and climate-change data.Source: §6.6 · p. 36

Framework proposal

• The synthesized framework links Agentic AI system qualities—including faster decisions, reliance, resource optimization, efficiency, scalability, data handling, and seamless service—to satisfaction, trust, reliability, use, and adoption.It is based on Wixom and Todd and is presented in Figure 7.Source: §7 · p. 36, Figure 7 · p. 36
• Figure 7 presents the synthesized framework for Agentic AI development and adoption.Source: Figure 7 · p. 36

Theoretical relationship

• The framework depicts direct effects of object-based beliefs on behavioral beliefs rather than requiring mediation through object-based attitudes.This design reflects empirical evidence alongside the mediated relationship discussed by Wixom and Todd [106].Source: §7 · p. 36

Research Agenda Method

• Problematization analysis organizes future research by significance, deriving feasible research questions from limitations in existing knowledge and the field’s development trends.Applications are largely theoretical or not implemented at scale, motivating consideration of frameworks, lay users, and regulation; summarized directions appear in Tables 8–13.Source: §8 · pp. 36–37

Ethical and Behavioral Considerations

• Ethical research should examine transparency, accountability, trust, explainability, unethical behavior, hermeneutic harm, emotional responses, and reactive attitudes in human–agent collaboration.Suggested methods include trust metrics, longitudinal studies, participatory design, explainable decision-tree visualizations, behavioral audits, and red-teaming for misaligned rewards in high-stakes domains.Source: §8 · p. 37

Data and Methodological Gaps

• Future studies should address data imbalance, limited diversity, controlled-testing versus real-world performance, fragmented domain knowledge, and the need for larger, cross-domain multimodal datasets.Field studies, longitudinal evaluations, human baselines, failure taxonomies, and cross-sectional or experimental research are proposed to validate generalizability and long-term applicability.Source: §8 · pp. 37, 43

Technical and Computational Gaps

• Technical research should develop scalable, computationally efficient, real-time learning architectures with improved reward parameterization, agent-interaction analysis, and domain-specific explainability.Candidate techniques include LIME, SHAP, and Anchors, evaluated experimentally with relevant stakeholders for resource-constrained and high-demand environments.Source: §8 · p. 43

Regulatory and Governance

• Governance research should establish secure communication protocols, security inspection mechanisms, industry-specific adoption strategies, and regulatory-compliant frameworks validated in regulated domains.Proposed approaches include systematic reviews, design thinking, participatory design, design science research, co-design, and clinical trials for healthcare standards.Source: §8 · pp. 43–44

Human-AI Collaboration and Cognitive Bias

• Human-centered research should improve collaboration by studying trust calibration, cognitive bias, social interaction, and digital-twin-supported human-agent workflows through human-in-the-loop experiments.Such studies can establish latency, social coherence, and success-rate benchmarks while embedding bias detection and reduction in collaborative systems.Source: §8 · p. 44

Domain Specific Agentic AI Research and Integration with Emerging Technologies

• Domain-specific research should validate Agentic AI through experiments, case studies, and pilots in transportation, healthcare, aerospace, finance, social media, smart grids, gaming, media, and IoT.Research should also evaluate interoperable integrations with IoT, blockchain, wearable sensors, quantum computing, and intelligent systems using qualitative, quantitative, and design-thinking methods.Source: §8 · pp. 44–46

• The review critically outlines Agentic AI’s historical perspectives and distinguishes it from other AI paradigms, providing a resource on its basics and advances.The authors state that such a comprehensive description has not yet been outlined in scholarly contributions.Source: §9 · p. 46
• The work details Agentic AI’s principles, components, architecture, and workflow in an accessible form for readers with basic AI knowledge.Source: §9 · p. 46
• It surveys empirical and proposed real-world Agentic AI applications across domains, showing what has been implemented and which application areas are emerging.These insights are intended to support AI researchers and practitioners in Agentic AI research and development.Source: §9 · p. 46
• The review analyzes Agentic AI adoption factors across disciplines, supporting feasibility studies, improvement efforts, and application design by entrepreneurs, investors, and developers.These factors are presented as indicators and motivations for real-world application.Source: §9 · p. 46
• It evaluates findings, applications, and research gaps, then organizes future research avenues into domain-specific and application-specific directions with tentative research questions.The authors identify this approach as distinct from other recent Agentic AI studies.Source: §9 · p. 46

• The systematic literature review searched only Scopus and Web of Science, excluding publisher-specific libraries and preprint servers such as arXiv.Source: §10 · p. 46
• The proposed architecture in Figure 7 was not validated, leaving validation as a future research opportunity for Agentic AI.Source: §10 · p. 46

• Agentic AI shows promising multidisciplinary implementation, but its applicability, feasibility, and adoption factors require further investigation.These considerations can also help practitioners adopt a more opportunistic perspective.Source: §11 · pp. 46–47
• Agentic AI has significant potential to automate complex tasks and enable numerous applications across different service-providing scenarios.Source: §11 · p. 47
• Future research directions are presented as useful for both practitioners and researchers, making continued Agentic AI research timely and necessary for advancing AI.Source: §11 · p. 47