The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges
Abstract
1. Introduction
1.1. Research Purpose
1.2. Research Questions
- RQ1
- How is agentic AI conceptually defined, and how does it differ from related paradigms?
- Ans:
- We define agentic AI’s core characteristics, distinguishing it from generative AI, autonomic computing, and multi-agent systems. A Venn diagram-based taxonomy illustrates overlaps and distinctions, clarifying terminology and providing a structured foundation.
- RQ2
- To what extent do current LLM-based and non-LLM-driven agentic AI, tools, and frameworks enable agentic capabilities?
- Ans:
- We analyze frameworks like LangChain, AutoGPT, BabyAGI, OpenAgents, Autogen, CAMEL, MetaGPT, SuperAGI, TB-CSPN, and non-LLM-driven agentic systems. Their features planning, memory, reflection, and goal pursuit are compared to evaluate how effectively current LLM-based tools enable agentic capabilities and approach true autonomy.
- RQ3
- What are the core components or architectural models used to build agentic AI systems?
- Ans:
- We define the core architectural components of agentic AI, emphasizing planning modules, memory system, and reasoning engines.
- RQ4
- What types of goals and tasks are currently being solved using agentic AI across domains?
- Ans:
- We provide a comprehensive classification of goals and tasks being addressed by agentic AI across multiple domains, along with their application contexts and corresponding systems used in the literature, presented in a tabular format.
- RQ5
- What kinds of input and output formats do agentic AI systems handle in comparison to traditional AI systems?
- Ans:
- We provide a detailed overview of the input and output formats handled by agentic AI systems, along with the specific tasks and corresponding applications, summarized in a tabular format, and compare this with the input–output mechanisms of traditional AI systems.
- RQ6
- What evaluation methods and metrics are used to assess the performance of agentic AI systems?
- Ans:
- We presented a classification for both the testing methods and the evaluation metrics to evaluate agentic AI systems. The evaluation metrics are further classified into qualitative and quantitative.
- RQ7
- What are the key challenges and limitations in designing and deploying agentic AI systems?
- Ans:
- We presented different challenges of agentic AI, including architecture and technical challenges, performance and tool integration issues, coordination between multi-agents, and user experience issues, along with ethical and security challenges.
1.3. Contributions and Research Significance
1.4. Organization of the Paper
2. Methodology
3. Results
3.1. How Is Agentic AI Conceptually Defined, and How Does It Differ from Related Paradigms?
3.2. To What Extent Do Current LLM-Based and Non-LLM-Driven Agentic Systems, Tools, and Frameworks Enable Agentic Capabilities?
3.2.1. LLM-Based Agentic Systems
3.2.2. Non-LLM-Driven Agentic Systems
3.3. Core Components and Architectural Models for Agentic AI
3.3.1. Core Functional Components of Agentic AI Systems
- Planning, Reasoning, and Goal Decomposition—Transforms goals into actionable steps, evaluates alternatives, and selects next actions [95].
- Execution and Actuation—Carries out actions via APIs or actuators, with monitoring and dynamic replanning [41].
- Reflection and Evaluation—Enables self-critique, verification, and refinement of actions and plans [50].
- Communication, Orchestration, and Autonomy—Coordinates task flow, retries, and timeouts, either centrally (e.g., LLM-based supervisor) or via decentralized protocols [50,96].This component stack recurs across both academic and industry implementations, including high-stakes domains like finance [39].
3.3.2. Architectural Models in Agentic AI
- ReAct Single-Agent:
- Supervisor/Hierarchical:
- Hybrid reactive–deliberative:
- Belief–Desire–Intention (BDI):
- Layered Decision (Neuro-Symbolic):
3.3.3. Coordination and Modularity in Agentic Architectures
- Modular Composition:
- Orchestration and Supervisory Control:
- Communication Protocols and Workflow Graphs:
3.3.4. Integration of Components into Architecture
- Layered and Modular Pipelines:
- Orchestration Mechanisms:
- Graph-based execution:
- Multi-Agent Integration and Decentralization:
3.3.5. Architectural Models vs. Core Components
3.4. What Types of Goals and Tasks Are Currently Being Solved Using Agentic AI Across Domains?
3.4.1. Healthcare
3.4.2. Military
3.4.3. Transportation
3.4.4. Software
3.4.5. Finance, Banking and Insurance
3.4.6. Manufacturing and Industrial
3.4.7. Tourism and Traveling
3.4.8. Multi-Domain
3.4.9. Scientific Discovery and Research
3.4.10. Retail, Business, and E-Commerce
3.4.11. Smart Cities and Energy
3.4.12. Public Administration
3.4.13. Education
3.5. What Kinds of Input and Output Formats Do Agentic AI Systems Handle in Comparison to Traditional AI Systems?
3.5.1. Text to Actions
3.5.2. Text to Text
3.5.3. Text to Multimodel Data
3.5.4. Audio Files to Text/Audio
3.5.5. Real-Time Data to Actions
3.5.6. Datasets to Text
3.5.7. Datasets to Actions
3.5.8. Dataset to Dataset
3.5.9. Dataset to Alerts
3.5.10. Multimodel Data to Multimodel Data
3.5.11. Beyond Conventional Input–Output Methods
3.5.12. Core Input–Output Mechanisms in Agentic vs. Traditional Systems
3.6. What Evaluation Methods and Metrics Are Used to Assess the Performance of Agentic AI Systems?
3.6.1. Testing Methods
3.6.2. Evaluation Metrics
3.7. What Are the Key Challenges and Limitations in Designing and Deploying Agentic AI Systems?
4. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Year Published | Number of Papers |
---|---|
1999 | 1 |
2005 | 1 |
2007 | 1 |
2014 | 1 |
2017 | 1 |
2018 | 1 |
2019 | 1 |
2020 | 1 |
2021 | 3 |
2022 | 6 |
2023 | 12 |
2024 | 21 |
2025 | 93 |
Total | 143 |
Paper Type | Number of Papers |
---|---|
Journals | 70 |
Preprints | 38 |
Conference Proceedings | 26 |
Articles | 2 |
Working papers | 2 |
Book | 2 |
Dissertation | 1 |
Workshop | 2 |
Total | 143 |
Publication Venue | Number of Papers | Reference Code |
---|---|---|
arXiv | 32 | [31,33,38,39,40,42,51,71,75,77,81,87,90,98,100,111,113,116,120,132,136,137,138,141,143,144,148,150,157,159,166,167] |
SSRN | 6 | [27,37,41,55,129,160] |
Preprints | 3 | [36,53,96] |
IEEE Access | 3 | [47,89,142] |
Companion Proceedings of the ACM on Web Conference | 3 | [45,112,139] |
IFAC-PapersOnLine | 3 | [61,72,119] |
AI | 2 | [30,88] |
ACM Conference on Fairness, Accountability, and Transparency | 2 | [34,168] |
Journal of Business Research | 2 | [131,146] |
Engineering Applications of Artificial Intelligence | 2 | [62,76] |
ACM International Conference on Intelligent User Interfaces | 2 | [52] |
Neural Information Processing Systems | 2 | [83,92] |
Metallurgical and Materials Engineering | 2 | [130,134] |
American Advanced Journal for Emerging Disciplinaries | 1 | [78] |
ACM Transactions on Software Engineering and Methodology | 1 | [110] |
ACM International Conference on User Modeling, Adaptation and Personalization | 1 | [97] |
Advanced Engineering Informatics | 1 | [124] |
Rani Channamma University Belagavi | 1 | [57] |
AIS Transactions on Human Computer Interaction | 1 | [114] |
American Journal of Computing and Engineering | 1 | [103] |
Annual review of psychology | 1 | [162] |
Applied Energy | 1 | [70] |
Architectural Intelligence | 1 | [169] |
Array | 1 | [66] |
Association for Computing Machinery | 1 | [79] |
Automation in Construction | 1 | [58] |
BioSystems | 1 | [158] |
Cell Reports Physical Science | 1 | [64] |
Chapman and Hall/CRC | 1 | [170] |
Clinical Neurophysiology | 1 | [99] |
Computer Methods and Programs in Biomedicine | 1 | [59] |
Computers and Electrical Engineering | 1 | [73] |
Computers in Human Behavior: Artificial Humans | 1 | [128] |
Cureus | 1 | [101] |
Current Opinion in Chemical Engineering | 1 | [43] |
Digital Discovery | 1 | [152] |
European journal of analytics and artificial intelligence | 1 | [35] |
Engineering | 1 | [82] |
European Management Journal | 1 | [68] |
Expert Systems with Applications | 1 | [135] |
Extreme Mechanics Letters | 1 | [165] |
PhD Dissertation | 1 | [32] |
Foreign Languages in Higher Education | 1 | [147] |
Frontiers in Computational Neuroscience | 1 | [122] |
Frontiers in Human Dynamics | 1 | [154] |
Informatics and Health | 1 | [56] |
Information and Organization | 1 | [67] |
International Journal of Computational Mathematical Ideas | 1 | [54] |
International Conference on the AI Revolution: Research, Ethics, and Society | 1 | [50] |
International Journal of Human-Computer Studies | 1 | [164] |
International Journal of Research Publication and Reviews | 1 | [107] |
Journal of Retailing | 1 | [140] |
Journal of Building Engineering | 1 | [126] |
Journal of Clinical and Experimental Hepatology | 1 | [102] |
Journal of Computer Information Systems | 1 | [161] |
Journal of Computer Science and Technology Studies | 1 | [95] |
Journal of Retailing and Consumer Services | 1 | [145] |
Journal of Strategic Information Systems | 1 | [149] |
Journal of Systems and Software | 1 | [133] |
Journal of Water Process Engineering | 1 | [63] |
MethodsX | 1 | [109] |
Multidisciplinary, Scientific Work and Management Journal | 1 | [80] |
NeurIPS 2024 Workshop on open-world Agents | 1 | [105] |
Optical Switching and Networking | 1 | [163] |
International Conference on Agents and Artificial Intelligence | 1 | [106] |
Procedia CIRP | 1 | [121] |
Conference on Human Factors in Computing Systems. | 1 | [155] |
Australasian Computer Science Week | 1 | [44] |
Special Interest Group on Management Information Systems—Computer Personnel Research. | 1 | [156] |
ACM International Conference on Autonomous Agents and Multiagent Systems | 1 | [74] |
ACM International Conference on Information and Knowledge Management | 1 | [108] |
ACM International Conference on Interactive Media Experiences | 1 | [60] |
ACM International Conference on Human Factors in Computing Systems | 1 | [153] |
International Conference on Automated Assembly Systems | 1 | [123] |
Pacific Asia Conference on Information Systems | 1 | [48] |
IEEE/ACM International Conference on Automated Software Engineering | 1 | [115] |
ACM on Software Testing and Analysis | 1 | [118] |
OpenAI | 1 | [46] |
Review of Materials Research | 1 | [65] |
Telecommunications Policy | 1 | [117] |
The Artificial Intelligence Business Review | 1 | [49] |
Tourism Management | 1 | [127] |
Transport Policy | 1 | [104] |
UIUC Spring 2025 CS598 LLM Agent Workshop | 1 | [151] |
Urban Informatics | 1 | [125] |
International Conference on Learning Representations | 1 | [84] |
Future Internet | 1 | [86] |
Springer Science and Business Media LLC | 1 | [91] |
Sage Publications, Thousand Oaks, CA | 1 | [94] |
Wiley Online Library | 1 | [93] |
SmythOS | 1 | [85] |
Total | 143 |
Appendix B
Acronym | Abbreviation |
---|---|
ABT | Agent-Based Traffic |
ACP | Adaptive Control Planning |
ASR | Automatic Speech Recognition |
BDD | Behavior-Driven Development |
BDI | Belief–Desire–Intention |
BERT | Bidirectional Encoder Representations from Transformers |
CAM | Compliance Agentic Model |
CAMEL | Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
CIF | Common Information Framework |
CLIPS | C Language Integrated Production System |
CRAB | Cross-Platform Agent Benchmark |
CTR | Click-Through Rate |
CUDA | Compute Unified Device Architecture |
DILI | Drug-induced liver injury |
DL | Deep Learning |
DQN | Deep Q-Networks |
EDX | Electrodiagnostic tests |
FM | Foundation Model |
GED | Graph Edit Distance |
GMV | Gross Merchandise Value |
ICA | Interactive Cognitive Agents |
JADE | Java Agent DEvelopment Framework |
LAM | Large Agent Model |
LC | Logic-based Computing |
LLMs | Large Language Models |
LLaMA | Large Language Model Meta AI |
LTM | Long-Term Memory |
MADRL | Multi-Agent Deep Reinforcement Learning |
MAAI | Multi-Agent Artificial Intelligence |
MAS | Multi-Agent Systems |
MASON | Multi-Agent Simulator Of Neighborhoods |
MCTS | Monte Carlo Tree Search |
ML | Machine Learning |
NLP | Natural Language Processing |
OOAD | Object-Oriented Analysis and Design |
PDDL | Planning Domain Definition Language |
PIBT | Priority Inheritance with Backtracking |
PIBTTP | Priority Inheritance with Backtracking for Tree-shaped Paths |
PPO | Proximal Policy Optimization |
RAG | Retrieval-Augmented Generation |
RL | Reinforcement Learning |
RLHF | Reinforcement Learning with Human Feedback |
SCADA | Supervisory Control and Data Acquisition |
SOC | Security Operations Center |
SOCRATEST | Self-Optimizing Contextual Reasoning and Adaptive Testing System |
STM | Short-Term Memory |
STRIPS | Stanford Research Institute Problem Solver |
TCT | Task Completion Time |
TB-CSPN | Task-Based Cognitive Sequential Planning Network |
UAT | User Acceptance Testing |
WWTP | Wastewater Treatment Plant Operation |
XAI | Explainable Artificial Intelligence |
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Reference | Year Published | Definition and Concept Taxonomy | Architecture | Applications Classification | Input/Output formats Classification | Evaluation Metrics Metric Classification | Challenges and Limitations | Remarks |
---|---|---|---|---|---|---|---|---|
[10] | 2021 | H | M | H | L | M | H | Review of agent-based programming languages and frameworks for multi-agent systems, bridging theory and practice while identifying future research directions. |
[11] | 2024 | M | L | H | NA | L | L | Focuses on use of LLMs and LLM-based agents, highlighting their applications, limitations, and future research directions across key SE tasks. |
[12] | 2024 | L | M | NA | L | NA | NA | Focus on unified taxonomy and decision model to enhance the architectural design, operational understanding of foundation-model-based AI agents. |
[13] | 2024 | H | M | H | NA | NA | H | Survey on unified framework detailing the architecture, applications, and challenges of LLM-based multi-agent systems. |
[14] | 2024 | H | H | M | M | NA | M | Focuses on unified framework for LLM-based autonomous agents, covering their construction, evaluation methods, and future challenges. |
[15] | 2024 | M | M | H | M | M | H | Survey on LLMs enhancing agent-based simulations across physical, cyber, social, and hybrid domains, highlighting challenges and future directions. |
[16] | 2024 | L | NA | L | NA | L | L | The study investigates U.S. public comfort with AI across occupations, showing attitudes vary by automation likelihood and individual traits. |
[17] | 2024 | L | L | L | NA | L | L | Focus multi-AI agent model using LLMs to fully automate and streamline the systematic literature review process. |
[18] | 2025 | M | H | L | M | NA | L | This paper explores 6G network integration with agentic AI, emphasizing automation and support intelligent, energy-efficient, low-latency operations. |
[19] | 2024 | M | H | L | L | M | M | This survey systematically reviews LLM-based agent planning, providing a taxonomy and analyzing recent approaches to improve planning abilities. |
[20] | 2025 | NA | NA | NA | NA | M | M | Focuses on reasoning methods in LLMs, offering a systematic framework to understand evolving LLM reasoning capabilities and trends. |
[21] | 2025 | M | M | L | L | M | H | Focus on LLMs and autonomous AI agents, covering benchmarks, frameworks, applications, and future research directions. |
[22] | 2025 | M | NA | L | NA | NA | L | This survey explores the evolution from generative AI to agentic AI, highlighting enhanced reasoning, autonomy, and key challenges for future research. |
[23] | 2025 | M | H | M | L | M | M | The paper proposes an agentic AI framework using LLMs and TDD to enhance reliability, scalability, and decision-making in retail systems. |
[24] | 2025 | M | M | L | L | M | H | Reviews trust, risk, and security management in LLM-based agentic multi-agent systems and proposes a roadmap for responsible deployment. |
[25] | 2025 | M | H | L | L | H | M | Survey on LLM-based planning, categorizing methods and discussing evaluation frameworks and future directions. |
[26] | 2025 | H | M | L | L | M | H | Reviews on LLMOps, AgentOps, and MLOps, outlining best practices and challenges for operationalizing generative AI systems. |
[27] | 2025 | L | H | H | L | M | M | Review of AGI and agentic AI, emphasizing their definitions, architectures, applications, and associated challenges. |
[28] | 2024 | L | M | NA | L | L | M | Review of AI-enhanced tools for SLRs, focusing on screening, extraction, and the integration of LLMs. |
[29] | 2025 | H | H | M | L | M | M | Focuses on taxonomy and comparative analysis of AI agents and agentic AI, highlighting their divergent architectures, applications, and challenges. |
[30] | 2025 | H | M | H | L | L | M | Reviews agentic AI applications in SMMEs, highlighting about enhancing efficiency, adaptability, and innovation through interconnected autonomous agents. |
This Paper | 2025 | H | H | H | H | H | H | Our paper focuses on all six areas and contributes significant results such as definition and concept taxonomy, architecture, applications classification, input/output format classification, evaluation metrics, challenges, and limitations. |
Aspects | Multi-Agent Systems (MAS) | Generative AI (GenAI) | Agentic AI | Ref. |
---|---|---|---|---|
Primary Function | Task division among multiple autonomous agents for collaboration/competition | Task division among multiple autonomous agents for collaboration/competition | Goal-oriented autonomous decision-making with multi-step workflow execution | [27,35,37,39,41,43,45,47,49,54,55,56] |
Decision-Making | Distributed across multiple agents with coordination mechanisms | Pattern-based responses from training data | Independent analysis, reasoning, and contextual decisions | [11,12,13,14,15,19,20,25,26,27,31,41,44,47,57,58] |
Task Scope | Complex goals broken into smaller tasks for individual agents | Single-turn interactions focused on content creation | Multi-step, multi-layered tasks over extended time frames | [10,27,37,47,59,60,61,62,63] |
Learning Approch | Individual agent learning with inter-agent communication | Static dataset learning during training phase | Interactive learning with continuous feedback loops and adaptation | [41,57,64,65] |
Architecture | Multiple coordinated agents with communication protocols | Primarily transformer-based neural networks | Planning modules, memory systems, tool integration, reasoning components | [27,35,47] |
Adaptability | Coordination challenges; varies by system design | Limited to training data patterns; requires retraining for adaptation | Dynamic adaptation to changing environments and goals | [27,41,47,57] |
Human Intervention | Varies by implementation; often requires coordination oversight | Moderate intervention for prompt engineering and guidance | Low intervention; operates autonomously with minimal supervision | [47,57,66,67,68] |
Goal Orientation | Distributed goal achievement through agent collaboration | Content generation goals defined by prompts | Self-defined and executed goal-oriented behavior | [18,22,23,24,27,33,34,37,38,41,47,48,48] |
Workflow Management | Distributed workflow across multiple agents | Cannot manage end-to-end workflows independently | Comprehensive end-to-end workflow orchestration | [37,69,70,71,72,73] |
Memory Systems | Individual agent memory with shared knowledge bases | No persistent memory between interactions | Sophisticated memory systems with reflection and experience storage | [35,37,39,41,74] |
Real-time Adaptation | Complex goals broken into smaller tasks for individual agents | Single-turn interactions focused on content creation | Multi-step, multi-layered tasks over extended time frames | [27,37,47] |
Use Cases | Complex distributed problem-solving, simulation systems | Content creation, text generation, image synthesis | Virtual assistants, autonomous systems, adaptive planning, scientific discovery | [37,47,56,75,76] |
Framework | Primary Purpose | Key Agentic Capabilities | Primary LLM Used | Ref. |
---|---|---|---|---|
LangChain | Building structured workflows with LLMs | Planning, memory, tool use | OpenAI, Cohere, Anthropic | [26,31,78,79] |
AutoGPT | Fully autonomous multi-step task execution | Goal pursuit, planning, reflection, tool use | GPT-4 | [43,45,47] |
BabyAGI | Accessible autonomous task management | Task decomposition, memory, adaptive execution | OpenAI | [49,81,82] |
OpenAgents | Multi-agent collaboration and coordination | Multi-agent reasoning, goal pursuit, iterative planning | GPT-4 | [26,78,79] |
Autogen | Coordinating multiple AI agents via dialogue | Multi-agent collaboration, planning, reflection | GPT-4 | [41] |
CAMEL | Simulating multi-agent dialogues with defined roles | Role-based interaction, dialogue simulation, collaborative exploration | GPT-4, LLaMA | [83] |
MetaGPT | Multi-agent collaborative problem solving | Automated problem decomposition, role assignment, coordination of agent societies | GPT-4 | [84] |
SuperAGI | Open-source framework for developing and deploying autonomous agents | Task orchestration, multi-agent workflows, deployment at scale | LLM-agnostic (commonly GPT-4, OpenAI, Anthropic, LLaMA) | [85] |
TB-CSPN | Context-aware agentic reasoning beyond prompt chaining | Task decomposition, constraint satisfaction, context-driven decision-making | No specific LLM | [86] |
Architecture | Perception/State | Planning and Reasoning | Memory | Execution and Action | Reflection/ Feedback | Orchestration/ Autonomy | Typical Uses | Salient Risks |
---|---|---|---|---|---|---|---|---|
BDI (Belief–Desire–Intention) | ✓ explicit beliefs | ✓ desire filtering, intention commitment | ∘ belief updates persistent | ✓ goal-driven loop | ∘ intention revision | ✓ central loop, commitment | Explainable decision systems; simulations | Symbolic modeling effort; brittle under high uncertainty [38,41] |
Hierarchical (HRL/modular) | ✓ local/implicit state | ✓ multi-level goal decomposition | ∘ episodic (reward/trace) | ✓ layered policies, sub-task execution | ∘ performance driven replans | ✓ supervisor(s) (top-down) | Decomposable, large programs; parallel teams | Supervisor bottlenecks; debugging across tiers [50,96] |
ReAct single-agent (LLM) | ✓ LLM-driven interpretation | ✓ stepwise CoT/ReAct | ∘ STM/LTM as needed | ✓ tool/API invocation | ∘ language based self-critique | ✓ local orchestration | Fast baselines; scoped assistants | Limited parallelism; tool-selection errors at scale [47] |
Hybrid (reactive–deliberative) | ✓ sensor + abstract model | ✓ deliberative planner | ∘ localized recall | ✓ reactive+ planned actions | ∘ arbitration triggered | ✓ Supervisor arbitration | Real-time ops with long horizon | Consistency across loops; arbitration design [38] |
Layered Neuro-Symbolic | ✓ neural perception+ inference | ✓ symbolic planner | ✓ memory for rules/episodes | ✓ planner to actuator tools | ✓ verification/reflection | ✓ structured orchestration | Open-world planning under uncertainty; public sector | Integration overhead; representation alignment [39,41,95] |
Domain | Application | Goal/Task Attempted by Agentic AI | Ref. |
---|---|---|---|
Healthcare | Diagnostics and treatment planning | Enhancing healthcare decision support, diagnosis, and personalization. | [59] |
Fitness Coaching | Personalized, adaptive fitness coaching using multimodal multi-agent digital twin systems. | [60] | |
Neuromuscular Electrodiagnostic | Standardized, AI-assisted interpretation and reporting of neuromuscular EDX tests for electrophysiologists. | [99] | |
Genomic analysis and dynamic health management | Personalized, real-time healthcare management using agentic AI for prediction, intervention, monitoring, and workflow optimization. | [35] | |
Ethical oversight in generative AI | Ethical, compliant, and trustworthy deployment of GenAI in healthcare via agentic oversight. | [44] | |
Computer vision (medical CV) | Autonomous construction and execution of medical image segmentation pipelines via agentic AI. | [100] | |
Bioethical clinical decision support | Advisory system for ethical clinical decision support in complex healthcare scenarios. | [101] | |
Clinical Decision and Drug Discovery | Transforming healthcare to enable accurate diagnostics, personalized treatment planning, real-time patient monitoring, workflow automation, and drug discovery. | [56] | |
Clinical Risk Assessment | Real-time identification and assessment of DILI risks using LLM on clinical notes. | [102] | |
Transportation | Traffic control in Smart Cities | Enhancing traffic control, safety, and sustainability in urban transportation systems. | [103] |
Industrial Systems –Transportation, and Biomanufacturing | Real-time optimization, control, and personalization in industrial and biomanufacturing systems. | [43] | |
Autonomous Vehicle Control and Safety | Multimodal model predictive control for safe, context-aware autonomous vehicle navigation. | [74] | |
Multi-Agent Routing and Scheduling | Multi-agent routing and scheduling optimization in logistics and guidepath networks. | [61] | |
Human-Machine Transportation Systems | Multi-agent management of transportation systems across lifecycle phases to improve travel outcomes, safety, and system adaptability. | [104] | |
Software | Automated query and task processing | Autonomously decompose complex queries, select relevant tools, and execute tasks efficiently with real-time feedback. | [105] |
BDD Test Case Generation | Automate BDD test case generation from natural language while supporting code mapping and user collaboration. | [106] | |
Computer vision in healthcare, robotics | Enable autonomous, real-time decision-making and adaptive behavior from visual data for complex perception and interaction tasks. | [107] | |
Software and task automation in SMMEs | Automate software engineering and business tasks through autonomous, goal-driven multi-agent systems. | [30] | |
Fraud and sensor drift management. | Automate and optimize data pipelines with adaptive, interpretable, and balanced agent-driven learning. | [54] | |
Personalized search and recommendations | Develop AI agents for personalized, relevant, and fair information retrieval across complex and multimodal queries. | [108] | |
CV task automation for non-expert users. | Automate CV tasks via natural language commands for non-experts. | [109] | |
Fault-Tolerant Multi-agent Software Systems | Design fault-tolerant software systems with autonomous multi-agent control and recovery. | [62] | |
Automatic Programming for End-Users | Enable end-user software development from natural requirements to deployment using LLMs. | [110] | |
SLM-based software and IT task automation | Automate software and IT tasks and coordinate multi-agent workflows using SLMs. | [111] | |
Personalized and autonomous IR systems. | Improve relevance, accuracy, and personalization in IR systems with autonomous task handling. | [112] | |
Autonomous software development | Automate multi-step software development and optimize workflows, code quality, and maintenance autonomously. | [50,51] | |
Web and file automation | Execute complex digital tasks with minimal oversight using modular multi-agent systems. | [113] | |
Human-algorithmic agency modeling | Clarify and theorize agency in human-algorithmic ensembles to inform organizational governance and socio-technical system design. | [114] | |
Conversational software testing agent | Develop SOCRATEST, an autonomous LLM-based conversational agent for software testing. | [115] | |
Conversational MLOps | Facilitate human–AI collaborative MLOps using conversational interfaces. | [116] | |
Agentic SOC automation | Automate SOC operations for proactive detection, mitigation, and response to cyber threats. | [117] | |
Autonomous IT operations and automation | Autonomous IT operations for real-time incident detection, prediction, and resolution. LLM-driven autonomous IT operations enabling adaptive, collaborative incident detection, prediction, and resolution. | [49,118] | |
Military and Security | Multi-Agent Attack-Defense Coordination | Autonomous swarm decision-making for coordinated attack-defense in military confrontations. | [119] |
AI Workloads and Infrastructure Defense | Automated moving-target defense for AI workloads through ephemeral infrastructure rotation in Kubernetes. | [120] | |
Manufacturing and Industrial | WWTP optimization | Optimize full-scale wastewater treatment plant operation to cut costs and improve water quality under varying conditions. | [63] |
Smart Manufacturing and robotic assembly | Develop autonomous, multi-agent systems to optimize manufacturing workflows, task sequencing, and adaptive production processes. | [33,121] | |
Automated Supply Chain Decisions | Enable autonomous decision-making in food supply chains to improve efficiency, safety, and reduce waste. | [55] | |
Cognitive robots for collaboration | Develop autonomous, cognitive robotic agents that can safely cooperate with humans, learn continuously, and plan for future scenarios. | [122] | |
Autonomic quality management | Achieve self-managing, autonomic manufacturing systems that reduce human effort, ensure compliance, and optimize execution for total quality achievement. | [123] | |
Human–Robot Collaboration | Predict 3D human motion to enable safe and efficient human–robot collaboration in construction tasks. | [124] | |
Multi-Agent Pickup and Delivery | Enable multi-agent pickup and delivery systems to complete tasks efficiently and without deadlocks in complex environments. | [69] | |
Smart Cities and Energy | Optimize city services and coordination | Autonomously manage, optimize, and adapt urban systems for resilient, equitable, and sustainable cities. | [125] |
Energy management and fault detection | Optimize building energy use, operations, and maintenance for efficiency, cost reduction, and reliability. | [58,70,126] | |
Public Administration | Resource and service management | Optimize governance, public services, and policy-making for efficiency, equity, and sustainability. | [37,41] |
Tourism and Traveling | Decision optimization in tourism | Optimize tourism management decisions using agentic AI for pricing and personalized services. | [127] |
Finance, Banking, and Insurance | Autonomous banking operations | Enhance financial decision-making, risk profiling, and automated banking operations using AI. | [80] |
Financial modeling and risk management | Automate financial modeling and model risk management workflows using LLMs to ensure compliance, robustness, and accurate decision-making. | [39] | |
AI for BFSI customer support | Assist consumers in BFSI by providing information, recommendations, decision aid, and delegated actions. | [128] | |
Risk and fraud detector | Enhance banking risk management and fraud using LLMs, ML, XAI. | [129] | |
Personalized banking | Provide trustworthy, personalized banking experiences and financial guidance through autonomous agentic AI. | [130] | |
AI-driven automated customer service | Enable AI-driven, autonomous customer service and decision-making to improve efficiency, personalization, and workflow automation in business and insurance contexts. | [96,131] | |
Multi- Domain | Personalized recommendations and task management | Develop foundation model-powered agents for autonomous, multi-domain task execution, combining personalized recommendations and general task automation with planning, reasoning, and collaboration. | [32,75,76,132,133,134] |
Multi-agent system | Enable autonomous, goal-driven AI agents and multi-agent systems to plan, act, reason, and adapt across multiple domains, completing complex, long-horizon tasks with minimal human intervention. | [31,34,36,47,53,77,135,136] | |
Coding and problem solving | Enable autonomous agents to reason, plan, and solve complex tasks across diverse domains. | [46,137] | |
Cost-efficient plan caching | Enable autonomous LLM agents to perform multi-step reasoning and planning efficiently across domains while reducing computational costs. | [66,138] | |
Radiology and predictive crime analytics | Enable autonomous, agentic AI systems to support decision-making, diagnostics, and automated scheduling in professional and medical domains. | [67] | |
Memory-Augmented Agent Systems | Enable LLM agents to perform multi-domain tasks with memory for reflection, recall, and long-term planning. | [139] | |
Retail, Business and E-commerce | Retail and supply chain automation | Automate and personalize retail customer interactions and supply chain operations. | [27,78,140] |
Digital task automation | Autonomously optimize digital tasks, schedules, and workflows across platforms using agentic AI. | [141,142] | |
Enterprise automation | Automate enterprise decision-making and workflows to enhance productivity and reduce errors. | [68,143,144] | |
Cognitive robots for collaboration | Develop autonomous, cognitive robotic agents that can safely cooperate with humans, learn continuously, and plan for future scenarios. | [122] | |
Customer service and retail support | Optimize AI chatbots for customer service, decision support, and human-agent collaboration in retail. | [145,146] | |
Interactive Recommender Systems | Deliver interactive, personalized, multi-turn recommendations using LLM-enhanced chatbots. | [79] | |
Automate tasks and coordinate AI agents | Enable AI agents to autonomously reason, plan, and act across enterprise systems while securely automating tasks. | [40] | |
Education | Personalized educational assistance | Enhance learning, teaching, and educational administration through personalized and adaptive support | [57,97,147] |
Evolution of AI in Education | Improve consistency, reliability, and fairness in automated essay grading using multi-agent systems. | [148] | |
Research and task management | Enhance research, decision-making, and cognitive support through intelligent task execution. | [149] | |
Scientific Discovery Research | Automated Scientific Research | Automate and accelerate scientific discovery across chemistry, materials science, bioinformatics, and molecular biology. | [45,150,151] |
LLM model battery simulation | Integrate LLMs with physical models to autonomously simulate, analyze, and guide advanced battery research. | [64] | |
LLM-driven materials discovery | Accelerate materials discovery using knowledge-guided LLMs with autonomous labs. | [65] | |
Science Exocortex for Research Automation | Augment human cognition and automate scientific research using a “science exocortex” of AI agents. | [152] | |
Automated interview and data coding | Automate and streamline qualitative interviews and analysis in social and health sciences. | [153] |
Input | Output | Task Performed | Technology | Ref. |
---|---|---|---|---|
Text | Actions | Process user prompts to run tasks and coordinates the agents | LLMs, MCP servers | [120] |
Processes and handles the user query to handling multi-step workflows | RAG systems, Vectara-agentic | [27] | ||
Manages live network operations and delivers network services | Autonomous Agents, Orchestration APIs | [82] | ||
Automatically generate efficient robotic assembly workflows | MAPE-K, ML, knowledgebase | [121] | ||
Design and implement fault-tolerant software | Multi-agent AI and concurrent SE techniques | [62] | ||
Enables direct network control as per Telecom control | Decision-making module | [71] | ||
Coordinate multiple home appliances for energy saving | MAS, enhanced by LLMs | [38] | ||
Decide next action by self-reflection or knowledge use and generate signals | LLMs, KnowSelf, DeepSpeed, vLLM | [144] | ||
Executes arbitrary computer tasks | LLMs, Adept ACT-1, AutoGPT, OpenAI’s tools | [34] | ||
Critical self-analysis and refinement of its previous outputs | LLMs, SELF-REFINE | [50] | ||
Learn from feedback, self-improve dynamically | BrainBody-LLM, RASC, REVECA, AIFP | [75] | ||
Resolve ambiguity and adapts to user preferences | LAM, Neuro-symbolic program, LLM | [154] | ||
Text | Parse user intent, maps, retrieve and combine data to respond back | Router Agent (LLM), domain APIs | [77] | |
Understanding/responding to inquiries quickly | AI agents, LLM workflows | [41] | ||
AI agent learns and shares this expertise with other agents | LLM, AI Agent with locally privacy-preserving learning | [143] | ||
Understand, search, compute, combine results | LLaMA-3-70B, Bee agent (ReAct) | [52] | ||
Complex problem-solving through iterative reasoning | GPT-4, GPT-3.5, Claude, Self-refinement, CoT | [137] | ||
Plans and handles multi-step support for customer interactions | GPT-3.5, service robots | [140] | ||
Assist software development by completing codes | LLM | [155] | ||
Generating and refining novel research ideas and hypotheses | LLMs, reinforcement learning, RAG | [150] | ||
Understanding, planning, and error correction in codes | GPT-4, fine-tuned LlaMA 2, ItemCF, SASRec | [110] | ||
Planning and generating executable synthesis plans | GPT-4, 18 tools, RoboRXN | [65] | ||
Splits big goals, step-by-step plans and achieve multiple targets | Autonomous HVAC agents | [37] | ||
Text/JSON scripts | Break down, assign, execute, and monitor tasks | LLM orchestrators, FAISS | [105] | |
JSON scripts | Automate research by assigning tasks to AI agents | RAG, LLM, citation traversal | [151] | |
Text/Charts | Battery material analysis, SEI growth prediction | AgentGPT, DFT, VASP, APIs, Cloud, SEI Models | [64] | |
YAML config file/image | Plan steps, generate YAML, verify, self-correct, execute | LLM, SimpleMind | [100] | |
Numerical data | Extract, plan, and refine financial data | GPT-4o-mini, LLaMa-3.2-8B | [138] | |
Text/numerical results | Clinical decision support for epilepsy detection | MAS, agent abstraction, ML | [59] | |
Logs/audit events | Workflow execution and data retrieval | LLM, RAG, LangChain, LangFlow, AutoGen, CrewAI | [40] | |
Audio | Text | Data review, ethical decision-making and gives advice | Agentic AI, LLMs, fairness tools | [101] |
Text/Audio | Goal parsing, context-aware feedback generation | ASR, STM, LTM, GPT-4 | [60] | |
Audio | Conduct structured, empathetic interviews | GPT-4o mini, Whisper3-turbo, Llama3.2 | [153] | |
Real-time data | Action | Analyze sensor data to detect issues and optimize operations | IIoT, ML, DT, Cloud | [131] |
Buildings adjust energy use to support the power grid | MADRL, AC, AM, DNN, CityLearn | [70] | ||
Multi-agent navigation and collision avoidance | PIBT+, Priority Decentralized Ctrl | [72] | ||
Monitor, analyze, plan, optimize to enhance system performance | MAPE-K loop, sensors, Machine Learning | [123] | ||
Smart nano-grid energy management | Fuzzy Logic Control, VSI, SAF | [73] | ||
Automated threat detection, response, and system optimization | Agentic AI, OOAD, CIF, DA | [156] | ||
Adjust plans in response to disruptions | Agentic AI with RL + MAS | [30] | ||
Learn through trial and error to improve strategies | RL, Q-learning, DQN, PPO | [36] | ||
Goal setting and strategic adaptation, decision-making | Urban sensing, LLMs | [125] | ||
Text/Dataset | Suggests better routes to prevent traffic and manage flow | ACP, ABT, AI/ML, DS, AI, VA, CB | [103] | |
Real-time coordinates | Actions | Autonomous task completion, movement decision-making | Swarm algorithms, Stepwise decisions | [119] |
Datasets | Text | Monitoring and providing feedback for employee performance | AI, NLP, SML, DL, MA | [146] |
Generate E-commerce product descriptions, updates and manages catalogs | Transformer model, PyTorch | [142] | ||
Learn and adjust prices to increase profits | Reinforcement Learning (RL), Q-learning | [157] | ||
Provide coaching and insights for team members | CoachBot, BRiN, Amanda | [145] | ||
Actions | Real-time feedback-based adaptation | MPA, A-Core integration | [66] | |
Analyze datasets, detect threats, autonomously mitigate threats | LLMs, agentic AI, AI agents, networks of specialized models | [117] | ||
Adapted task performance | LLMs, RLHF, RAG | [152] | ||
Automated trading and investment | Agentic AI, Neural Nets, Trading algs | [80] | ||
Optimize processes, identify opportunities | Agentic AI, Agentic platform, Pred. analytics, adaptive reasoning | [158] | ||
Make quick decisions to adjust plant controls, improves results | MARL and G2ANet | [63] | ||
Dataset | Predictive policing and analysis | CAS, ML algorithms | [67] | |
Extracts datasets split into training/testing sets and subsamples | Code execution tool + GPT-3.5 Turbo | [39] | ||
Data analysis and pricing strategy formulation | Explainable AI (XAI), Rule-based logic | [127] | ||
Alerts | Monitor data, detect risks, notify | Autonomous AI system with LLMs | [102] | |
Fraud detection and risk management | AI, ML, LLMs, NLP, GNN | [129] | ||
Images/Visuals | Communicate complex info visually | Kubernetes bots, OSSD bots, Gatekeeper | [32] | |
Dataset/Text | Analyze and predict health outcomes | Agentic AI, LLM | [35] | |
Text/Images | Text | Review policy, verify claims, craft response, coordinate agents | Multi-agent AI, Pretrained LMs, RAG | [96] |
Diagnosis issue, generates treatment plan, and healthcare process | LLMs, DL, ML, multimodal AI | [56] | ||
Text/Dataset | Text | Create, modify, and guide BDD test steps with user feedback | LLMs, NLP, RAG, ReAct agent, BDD frameworks, Streamlit UI | [106] |
Text/Test code | Autonomous software testing, Planning and executing tests | MW, LLMs, MA, LC, ACM, NN | [115] | |
Text, JSONL data | Generates personalized, contextual data and synthetic data | LLaMA 3.2 3B, LoR, JSONL | [97] | |
Text/Actions | User intent retrieval and action mapping | LLMs, RAG, RL, Knowledge Graphs | [159] | |
Text Images Dataset | Text/Code/Logs | Problem-solving, adaptive execution, information gathering | Agentic AI/LLM-based assistant | [46] |
Text/Audio/Actions | Personalized service, actions monitoring, fault detection | Agentic AI, NLP, ML, DL, RL | [160] | |
Images/Video/Text | Actions | Predict and align with human intent to adapt robot behavior | LSTM-based deep learning | [124] |
Images/Video | Coordinates/Image | Object detection, classification, result refinement | YOLO, ResNet, LLMs, RAG, VCG16 | [109] |
Video/Audio | Actions | Perception, reasoning about complex situations | MM pipelines, adaptive mechanisms; spatial and temporal reasoning | [95] |
Text/Images/Audio | Actions | Adaptive decision-making and optimized task execution | LLMs, VLMs, RL, Embodied AI | [135] |
Text/Images/Audio/Video | Actions | Strategy refinement and optimization | Self-supervised and RL | [33] |
Image/Real-time data | Actions | Real-time prediction and adjustment of navigation | Agentic AI, DRL, computer vision | [107] |
Text/Audio | Actions | Plan and execute complex tasks like booking hotel rooms | GPT-4, APIs, web automation | [141] |
Text/Audio/Image | Actions | Understand and fulfill user needs by NPL interaction | CLIP, Multimodal Foundation Models | [132] |
Text/Images/Audio/Graphs/Charts | Graphs/charts | Recommend products, provide feedback, and manage tasks | Agentic knowledge graphs, ML | [134] |
Text/Image/Audio | Text/Image/Audio | Customer query and feedback handling | Transf. models, PT, CUDA | [42] |
Text/Video/Audio | Text/Visual analytics | Real-time adaptation, intelligent decision-making | RL, NLU, contextual reasoning | [57] |
Graph/Code/Text | Numeric data | Predicts and ranks workflow performance without execution | GNNs, MLPs, T5/BERT (MiniLM) | [81] |
Numeric + Text | Word document | Compile FDD results and analyses to comprehensive report | AI Agent, Code Interpreter, LangSmith | [126] |
Spatial coordinates | Actions | Agents plan collision-free moves to complete tasks | Decentralized control algorithms, PI, BT, PIBTTP, PIBTTP-TA | [69] |
Numeric | Actions | Adjusting crystallization residence time and optimizes emission | Data denoising, SCADA integration | [43] |
Alert | Actions/Alerts | Analyze alerts, mitigate risks, manage Tier 1/2 responses | Agentic AI with generative AI | [161] |
Code files like .cs | Code files | Modifies code, generates git diffs, logs, summaries | Codex (OpenAI), git diff, sandbox | [51] |
CSV file | ML model(.pt,.pkl) | Generate and debug energy modeling code | gpt-4o on LangGraph (ReAct), PythonREPL | [58] |
PDFs | Dataset | Extract and organize clinical data | AANEM references, LLM Gemini 1.5 API | [99] |
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Bandi, A.; Kongari, B.; Naguru, R.; Pasnoor, S.; Vilipala, S.V. The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet 2025, 17, 404. https://doi.org/10.3390/fi17090404
Bandi A, Kongari B, Naguru R, Pasnoor S, Vilipala SV. The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet. 2025; 17(9):404. https://doi.org/10.3390/fi17090404
Chicago/Turabian StyleBandi, Ajay, Bhavani Kongari, Roshini Naguru, Sahitya Pasnoor, and Sri Vidya Vilipala. 2025. "The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges" Future Internet 17, no. 9: 404. https://doi.org/10.3390/fi17090404
APA StyleBandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025). The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges. Future Internet, 17(9), 404. https://doi.org/10.3390/fi17090404