Comprehensive Review of Artificial General Intelligence AGI, Agentic AI and GenAI: Current Trends and Future Directions
Satyadhar Joshi
Independent,Alumnus,InternationalMBA, Bar-IlanUniversity,Israel
*CorrespondingAuthor: Satyadhar Joshi
Article Info
ISSN (online): 2582-7138
Volume: 06
Issue: 03
May-Jun 2025
Received: 08-03-2025
Accepted: 05-04-2025
Page No: 681-688
Abstract
This paper presents a comprehensive review of Artificial General Intelligence (AGI) and Agentic AI, examining their technological foundations, current capabilities, and future trajectories. The study identifies key technical distinctions between these AI paradigms, including their architectural requirements, computational demands, and learning mechanisms. We survey the current technical landscape, including specialized frameworks like OpenAI’s AGI classification system and emerging Agentic AI platforms such as Vectara-agentic and CrewAI. The paper also examines the hardware infrastructure and cloud services enabling these advanced AI systems, from NVIDIA’s specialized GPUs to large-scale projects like OpenAI’s proposed "Stargate" initiative and others. Our comparative analysis reveals that Agentic AI is rapidlytransitioningfromresearchtopracticaldeploymentacrossindustriesincluding legalservices,DevOps,andenterpriseautomation,whileAGIremainsintheresearch phase with ongoing debates about its feasibility and timeline. The paper discusses critical challenges in both domains, including safety considerations, alignment problems, and governance requirements. We highlight how Agentic AI serves as a bridge between today’s generative AI capabilities andfutureAGIaspirations,offering autonomous functionality while avoiding some of AGI’s unresolved risks.
DOI: https://doi.org/10.54660/.IJMRGE.2025.6.3.681-688
Keywords: ArtificialGeneralIntelligence,AGI,AgenticAI,GenAI,AutonomousSystems,AISafety
1. Introduction
The field of artificial intelligence is undergoing rapid transformation, with two particularly significant developments capturing researchers’ attention: the pursuit of Artificial General Intelligence (AGI) and the emergence of Agentic AI systems [1, 2].While AGI represents the long-standing goal of creating machines with human-level general intelligence [3], Agentic AI focuses on developing autonomous systems capable of complex decision-making within specific domains [4]. Recent advancements have reigniteddebatesaboutthefeasibilityandtimelineforachievingAGI.SomeexpertspredictAGIcouldemergeasearlyas2025 [5, 6], while others remain skeptical, arguing that fundamental challenges remain unresolved [7, 8]. Meanwhile, Agentic AI has gained traction as a practical approach to creating more autonomous and capable AI systems [9, 10]. This paper examines the current state of both AGI and Agentic AI, their relationship, and their potential impacts on industry and society. Section II exploresdefinitionsandkeyconcepts.SectionIIIanalyzesrecenttechnologicaldevelopments.SectionIVdiscusseschallenges andlimitations.SectionVexaminesapplicationsandimplications,andSectionVIconcludeswithfuturedirections.
2. Introduction to Technical Concepts and Terminology
Thissectiondefinesandexplainsmoretechnicaltermsandconceptsidentifiedinthereviewedliterature.
A) Definitions and Key Concepts
1. Artificial General Intelligence (AGI)
AGI refers to artificial intelligence systems that possess the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence [3, 11, 58] . Unlike narrow AI systems designed for specific tasks, AGI would demonstrate flexible, generalpurposeintelligence [12]
The pursuit of AGI represents what some consider the "holy grail"ofAIresearch [13]. However,significantdebatepersists about both the definition of AGI and the path to achieving it [14]
2. Agentic AI
Agentic AI represents a paradigm shift from passive AI systems to autonomous agents capable of initiating actions, making decisions, and completing complex workflows without constant human supervision [2, 15]. These systems go beyond generative AI capabilities by incorporating goaldirectedbehaviorandfunctionalautonomy [4, 16] .
Key characteristics of Agentic AI include
Autonomousdecision-makingcapabilities [17]
Abilitytohandlemulti-step workflows [18]
Capacity for self-improvement within defined parameters [19]
Contextualunderstandingand adaptation [20]
B) Technical Concepts and Terminology
1. Artificial General Intelligence (AGI): Systems with human-level understanding and reasoning across diverse domains [3].Characterizedby:
Implemented through
Differentiableneuralcomputers(DNCs)
Memory-augmentedneuralnetworks(MANNs)
5. Level AGI Classification: OpenAI’s framework for measuringprogresstoward AGI[23]:
Where �������� represents general capability, ���� is task performance,and ���� isadaptationspeed.
2.AgenticAI:Autonomoussystemscapableofgoal-directed behavior and multi-step workflows [2]. Autonomous systems capable of goal-directed behavior and multi-step workflows [2]. Key components include function calling architectures, workflow orchestration engines, and reinforcementlearningfromhumanfeedback(RLHF).
Key components include
Functioncallingarchitectures
Workfloworchestrationengines
Reinforcementlearningfromhumanfeedback(RLHF)
3. Multi-Paradigmatic AI: Hybrid approaches combining neuralnetworkswithsymbolicreasoning [21].Expressedas:
4. Episodic Memory in AI: Biologically-inspired memory systems enabling contextual learning [22] Biologicallyinspired memory systems enabling contextual learning [22] Implementations include differentiable neural computers (DNCs)andmemory-augmentedneuralnetworks(MANNs).
Level1:BasicconversationalAI
Level2:Competentassistants
Level3:Autonomousagents
Level4:Innovatingsystems
Level5:Superintelligence
6. Agentic RAG: Retrieval-Augmented Generation systems withautonomouscapabilities [24].Architectureincludes:
Where �� processes queries, �� handles documents, and �� generatesactions.
7. Function Calling:CriticalcapabilityenablingAgenticAI to interact with external systems [4] A critical capability enabling Agentic AI to interact with external systems [4], implemented through API orchestration layers and tool-use architectures.
Implemented through
APIorchestrationlayers
Tool-usearchitectures
8. Autonomous DevOps: Application of Agentic AI to continuousintegration/deployment [10].Keymetrics:
9. Neuro-Symbolic Integration: Combining neural networkswithsymbolicAIforAGI [21].Representedas:
10. AI Safety Benchmarks: Metrics for evaluating dangerous capabilities in advanced AI [25].Metrics for evaluating dangerous capabilities in advanced AI [25], including self-improvement potential, goal misalignment risk,anddeceptioncapabilities.
Critical dimensions include
Self-improvementpotential
Goalmisalignmentrisk
Deceptioncapabilities
These concepts represent the foundational technical vocabulary emerging from current AGI and Agentic AI research. Their continued development will shape the evolutionofadvancedAIsystemsincomingyears.Thiswork isabuildupofourpreviouswork [59-73]
3. Recent Technological Developments A) AGI Progress and Benchmarks
OpenAI has proposed a 5-level framework for measuring progresstowardAGI,withcurrentsystemsestimatedtobeat
Level 1 [23, 26]. The company has also developed new benchmarkstoassessAGIpotentialandassociatedrisks [25] Recentresearchhasexploredmulti-paradigmaticapproaches to AGI development [21], including the incorporation of episodic memory systems inspired by human cognition [22] Meanwhile, some organizations are moving away from the termAGIduetoitscontroversialnature [27]
B) Agentic AI Advancements
Agentic AI systems are demonstrating increasingly sophisticatedcapabilitiesinvariousdomains:
DevOpsandKubernetesmanagement [10]
Legalservicesanddocumentanalysis [28]
Retailandcommerceapplications [29]
Enterpriseautomationand workflowmanagement [30]
Frameworks like Vectara-agentic are enabling the development of Agentic RAG (Retrieval-Augmented Generation)applications [24],whileplatformslikecrewAIare facilitatingcollaborativeAIagentsystems [31]
C) Industry Adoption and Investment
Major technology companies are investing heavily in both AGI research and Agentic AI applications. OpenAI has outlined an ambitious path toward superintelligence while maintaining nonprofit oversight [32], [33]. Deloitte surveys indicate growing enterprise interest in Agentic AI solutions [34], with some experts viewing it as a more practical neartermfocusthanAGI [35]
4. Technical Landscape: Tools, Libraries, and Infrastructure
The rapid evolution of Artificial General Intelligence (AGI) and Agentic AI has spurred the emergence of a diverse technical ecosystem encompassing specialized frameworks, libraries, and infrastructure. This section surveys the current landscape, drawing on recent literature and industry developments.
A) Frameworks and Libraries for AGI Development
AGI research is supported by a suite of advanced frameworks and libraries:
OpenAI’s Ecosystem: The GPT architecture series (e.g., GPT-4 and successors) forms a foundation for AGI research, with tools like the GPT-4 API and ChatGPT’s Operator Mode enabling experimentation withincreasinglygeneralcapabilities [36], [37]
DeepMind’s Symphony: DeepMind integrates TensorFlow, JAX, and proprietary architectures such as those used in AlphaFold, advancing AGI-oriented researchthroughamulti-frameworkapproach [38]
Hybrid and Neuro-Symbolic Approaches: Recent progress includes hybrid architectures that combine neuralnetworkswithsymbolicreasoning,supportedby libraries such as PyTorch Geometric and DeepGraphLibrary [21]. Memory-augmented systems leveragetoolslikeFAISSfor episodicmemory [22]
B) Agentic AI Toolkits and Platforms
Agentic AI emphasizes autonomous, goal-directed systems, leading to new categories of toolkits:
Vectara-agentic: A Python package designed for building agentic Retrieval-Augmented Generation
(RAG) applications, featuring built-in orchestration andautonomy [24]
CrewAI: An open-source framework supporting the creation of collaborative AI agents with role specializationandtaskdelegation [31] .
AutoGen: Developed by Microsoft, AutoGen enables theconstructionofcomplexmulti-agentconversational systems,supportingcustomizableagentbehaviors.
AgentGPT: A browser-based platform for deploying autonomous agents capable of dynamic goal-setting andexecution.
Synechron’s Agentic Stack: A proprietary platform that combines large language models (LLMs) with functioncallingand workflowautomation [4]
C) Cloud Services and Deployment Infrastructure
Major cloud providers are instrumental in supporting both AGI and Agentic AI workloads:
AWS Bedrock: Offers access to foundation models and agentic workflow tools, including Agents for AmazonBedrock.
Microsoft Azure AI Studio: Provides orchestration capabilities for multi-agent systems and seamless integrationwithcognitiveservices.
Google Cloud Vertex AI: Enables custom model training with TPU acceleration and supports agentic workflowpipelines.
NVIDIA AI Foundations: Delivers generative AI models and agentic tools as cloud services, leveraging advanced hardware such as the H100 and B100 GPUs [18] .
Specialized Hardware: Research systems increasingly utilize high-end accelerators (e.g., NVIDIA H100/B100, Cerebras Wafer-Scale Engines, SambaNovadataflowunits) to meetthecomputational demandsofAGIandagenticworkloads.
D) Computational Requirements and Hardware
The development and deployment of advanced AI systemsrequire significantcomputationalinfrastructure:
Training Infrastructure: Large-scale AGI experiments rely on GPU/TPU clusters with highspeed interconnects (e.g., NVLink, InfiniBand). Projects like OpenAI’s "Stargate" envision massive, dedicatedAIinfrastructure [39] .
Edge Deployment: Agentic AI is increasingly deployed on edge devices using frameworks such as TensorRT-LLM for optimized inference on platforms likeNVIDIAJetson.
Quantum Hybrid Approaches: Some research exploresquantum-classicalhybridsystemsforspecific AGI components, utilizing platforms like IBM QuantumandAmazonBraket.
Energy Efficiency: New architectures emphasize powerefficiencyviamixture-of-experts(MoE)models andsparsitytechniques [40] .
E) Benchmarking and Evaluation Tools
Measuring progress in AGI and Agentic AI requires robust benchmarking tools:
OpenAI’s AGI Levels: A five-level classification systemfortrackingAGIprogress [23]
Agentic Capability Metrics: Frameworks such as
Outshift’s 5 Levels of Agentic AI Intelligence offer enterprise-focusedevaluationcriteria [9]
AGI Safety Benchmarks: New evaluation suites assess dangerous capabilities and alignment properties [25] .
Multi-Agent Testing Environments: Platforms like NetHack and Minecraft provide rich, interactive environmentsfortestingagentic behaviors [19] .
F) Frameworks and Libraries for AGI Development
The pursuitofAGI has driventhedevelopmentofseveral specialized frameworks and libraries:
OpenAI’s Ecosystem: The GPT architecture series (including GPT-4 and beyond) serves as foundational modelsforAGIresearch [36].OpenAIhasreleasedtools like the GPT-4 API and ChatGPT’s Operator Mode as steppingstonestoward AGI [37]
DeepMind’s Symphony: DeepMind employs a combination of TensorFlow, JAX, and proprietary frameworks like AlphaFold’s architecture for AGIorientedresearch [38]
Multi-Paradigm Approaches: Recent work explores hybrid architectures combining neural networks with symbolic reasoning systems [21]. Frameworks like PyTorch Geometric and DeepGraphLibrary enable graph-basedreasoning.
Memory Architectures: Systems implementing episodic memory use modified versions of FAISS (Facebook AI Similarity Search) and specialized memorynetworks [22]
G) Agentic AI Toolkits and Platforms
Agentic AI development has spawned specialized tooling ecosystems:
Vectara-agentic: A Python package for building Agentic RAG (Retrieval-Augmented Generation) applicationswithbuilt-inorchestrationcapabilities [24]
CrewAI: An open-source framework for creating collaborative AI agents that can work in teams, supportingrolespecializationandtaskdelegation [31] .
Autogen: Microsoft’s framework for creating multiagent conversational systems with customizable agent behaviors.
AgentGPT: Browser-based platform for creating and deploying autonomous AI agents with goal-setting capabilities.
Synechron’s Agentic Stack: Proprietary platform combining LLMs with function calling and workflow automation [4]
H) Cloud Services and Deployment Infrastructure
Major cloud provider’s offer specialized services for AGI and Agentic AI:
AWS Bedrock:Providesfoundationmodelaccessand agenticworkflowtoolsthroughserviceslikeAgentsfor AmazonBedrock.
MicrosoftAzureAIStudio:Offersorchestrationtools for multi-agent systems and cognitive services integration.
Google Cloud’s Vertex AI: Features custom model training with TPU acceleration and agentic workflow pipelines.
NVIDIA AI Foundations: Cloud service providing
access to NVIDIA’s generative AI models and agentic tools[18].
Specialized Hardware: Deployment often utilizes NVIDIA H100 and upcoming B100 GPUs, with some research systems employing Cerebras Wafer-Scale EnginesorSambaNovareconfigurabledataflowunits.
I) Computational Requirements and Hardware
The development of advanced AI systems demands significant computational resources:
Training Infrastructure: Large-scale AGI experiments require GPU/TPU clusters with highspeed interconnects (NVLink, InfiniBand). OpenAI’s "Stargate" project proposes a $500B AI infrastructure initiative [39]
Edge Deployment: Agentic AI systems increasingly leverage edge devices through frameworks like TensorRT-LLM for optimized inference on NVIDIA Jetsonplatforms.
Quantum Hybrid Approaches: Some research exploresquantum-classicalhybridsystemsforspecific AGI components, using platforms like IBM Quantum orAmazonBraket.
Energy Efficiency: New architectures focus on reducing power consumption through techniques like mixture-of-experts(MoE)modelsandsparsity [40]
J) Benchmarking and Evaluation Tools
Assessing progress toward AGI requires specialized measurement tools:
OpenAI’sAGILevels:A5-levelclassificationsystem for measuringprogresstoward AGI [23] .
Agentic Capability Metrics: Frameworks like Outshift’s 5 Levels of Agentic AI Intelligence provide enterprise-focusedevaluationcriteria [9] .
AGI Safety Benchmarks: New evaluation suites measure dangerous capabilities and alignment properties [25]
Multi-Agent Testing Environments: Platforms like NetHackand Minecraft serve asrichenvironments for testingagenticcapabilities [19]
K) Summary and Outlook
The technical landscape for AGI and Agentic AI is characterized by rapid innovation across open-source and proprietary frameworks, cloud and hardware infrastructure, and rigorous benchmarking tools. Agentic AI toolkits are bridging the gap between narrow generative models and the broader ambitions of AGI by enabling autonomous, multistep workflowsandseamlesstoolintegration [2, 4, 24].Asboth research and industry adoption accelerate, the ecosystem is expectedtobecomeincreasinglymodular,collaborative,and capableofsupportingcomplex,real-worldapplications.
5. Applications and Implications
A) Business and Industry Impact
The emergence of AGI and Agentic AI is reshaping business strategies across sectors:
Retailandcommercetransformation [29]
Legalservicesautomation [20]
Enterpriseworkflowoptimization [41]
Newstartupopportunitiesand businessmodels [42]
Deloittesurveysindicatethat78%oftechleadersseeAgentic AI as a key enabler of sustainable value [34], while PwC analysis suggests AI is fundamentally rewriting competitive playbooks [41]
B) Societal Implications
The development of advanced AI systems carries broad societal implications:
Workforcetransformationand jobmarketimpacts [43]
Changes to innovation processes and scientific discovery [44]
New requirements for education and skills development [45]
Evolvinglegalandregulatoryframeworks [28]
6. Comparative Analysis of AI Paradigms
A) Taxonomy of Artificial Intelligence
Modern AI systems can be categorized into several distinct paradigms with varying capabilities:
Traditional/Narrow AI: Task-specific systems designed for particular applications (e.g., recommendation engines, computer vision) [12]. These representthemajorityofcurrentdeployed AIsystems.
Generative AI (GenAI): Systems capable of creating novel content (text, images, code) based on learned patterns [16, 46]. Examples include GPT models and StableDiffusion.
Agentic AI: Autonomous systems that can plan, make decisions, and execute multi-step workflows [2, 15] . These extend beyond generation to include actionorientedcapabilities [17]
Artificial General Intelligence (AGI): Hypothetical systems with human-level generalization across diverse domains [3, 11]. AGI remains unrealized but is activelyresearched [6]
B) Capability Comparison
ComparisonofAIParadigmsisshowninthetable
Table 1: Comparison of AI Paradigms
Characteristic Narrow AI GenAI Agentic AI AGI Task Scope Single Multiple Multiple Universal Autonomy None Low High Complete Creativity None High Moderate High Reasoning Limited Patternbased Goaloriented Human-like Learning Static Continuous Adaptive General Current Status Deployed Deployed Emerging Research
C) Technical Distinctions
The paradigms differ fundamentally in their architectures and requirements:
Data Requirements: While narrow AI and GenAI typically require large, domain-specific datasets [45] , AGI systems aim for efficient learning from diverse data [47] Agentic AI adds reinforcement learning from environmentalfeedback [19]
Architectural Complexity: GenAI primarily uses transformer architectures [48], while Agentic AI incorporates planning modules and memory systems [22]. AGI research explores hybrid neuro-symbolic approaches [21].
Computational Demands: GenAI systems require
massive inference resources, but Agentic AI adds ongoing computation for decision-making [10]. AGI wouldtheoreticallyneedunprecedentedscale [39] .
D) Use Case Differentiation
The paradigms excel in different application domains:
Narrow AI: Optimized for specific tasks like fraud detectionorpredictivemaintenance [49] .
GenAI:Idealforcontentcreation,codegeneration,and dataaugmentation [16]
Agentic AI: Suited for autonomous customer service, DevOps automation [10], and legal document analysis [20] .
AGI: Potential future applications in scientific discoveryandcomplexproblem-solving [43]
E) Evolutionary Perspective
The development trajectory shows increasing capability:
1. First Wave: Narrow AI systems (2010s) focused on specifictasks [12]
1. Second Wave: GenAI (2020s) demonstrated creative generation [46] .
2. Third Wave: Agentic AI (2024+) introduces autonomousaction [4] .
3. Future:PotentialAGIwouldrepresentqualitativeleap incapability [13]
F) Safety Considerations
Each paradigm presents distinct challenges:
Narrow AI: Bias in training data and overfitting JonesAGIHypeDamaging2024?
GenAI:MisinformationrisksandIPconcerns [28] .
Agentic AI: Unintended consequences of autonomous actions [50]
AGI:Existentialrisksandalignmentproblems [38]
Thiscomparativeanalysisrevealsthatwhiletheseparadigms share technological foundations, they represent fundamentally different approaches to artificial intelligence withdistinctcapabilitiesandapplications [51, 52].TheAIfield continues to evolve rapidly, with Agentic AI emerging as a practical middle ground between today’s GenAI and future AGIaspirations [9] .
7. Challenges and Limitations
A) Technical Challenges
The path to AGI faces several significant technical hurdles:
Developing systems with true understanding and reasoningcapabilities [53]
CreatingAIthatcangeneralizeacrossdiversedomains [49]
Implementing robust memory and learning mechanisms [47]
Achievinghuman-leveladaptabilityandcreativity [48]
B) Agentic AI systems face their own set of challenges
Ensuringreliableautonomousoperation [19]
Maintainingappropriatehumanoversight [50]
Managingcomplexmulti-agentinteractions [54]
Balancingautonomywithsafetyconstraints [38]
C) Safety and Ethical Considerations
Both AGI and advanced Agentic AI systems raise important safety concerns
Potentialfor misuseorunintendedconsequences [36]
Alignmentwithhumanvaluesandintentions [40]
Governanceandcontrolmechanisms [39]
Long-termsocietalimpacts [42]
Recent proposals emphasize the need for urgent safety measuresasthesetechnologiesadvance [38, 55] .
8. Conclusion
WhileAGIremainsanambitiousandpotentiallydistantgoal [7], Agentic AI represents a practical stepping stone that is already delivering value across industries [9, 56]. The coming years will likely see continued progress in both areas, with severalkeydevelopmentsonthehorizon:
Refinement of AGI benchmarks and measurement frameworks [57]
ExpansionofAgenticAIapplicationsacrosssectors [18]
Increasedfocusonsafetyand governance mechanisms [38]
Development of hybrid systems combining generative andagenticcapabilities [46]
As these technologies evolve, it will be crucial to maintain balanced perspectives that acknowledge both their potential and limitations. Future research should focus on developing robust evaluation methodologies, safety protocols, and practical implementation frameworks to ensure these powerful technologies deliver maximum benefit while minimizingrisks.
Thiscomprehensivereviewhasexaminedthecurrentstateof Artificial General Intelligence (AGI) and Agentic AI, analyzing their technical foundations, capabilities, and trajectories. Our investigation reveals several key insights abouttheevolvingAIlandscape:
First,whileAGIremainsanaspirationalgoalwithsignificant technicalhurdles [7, 8],Agentic AI hasemerged asapractical intermediate step that is already demonstrating real-world value [9, 10]. The development of frameworks like OpenAI’s 5-levelAGIclassificationsystem [23] andspecializedAgentic AI platforms [24] indicates growing sophistication in both domains.
Second, the comparative analysis highlights fundamental differencesbetweenAIparadigms.Wheretraditionalnarrow AI excels at specific tasks and generative AI at content creation, Agentic AI introduces autonomous decisionmaking capabilities [2], while AGI promises (but has not yet achieved) human-level generalization [3]. This spectrum of capabilities suggests a maturation path for AI systems, with Agentic AI serving as a crucial bridge between current technologiesandfutureAGIaspirations [51]
Third, the technical requirements for these advanced AI systems are becoming increasingly demanding, from specializedhardwarearchitectures [39] tonovelcloudservices supporting autonomous agent deployment [18]. These infrastructure developments both enable and constrain progressinthefield.
Lookingahead,threecriticalprioritiesemergeforresearchers and practitioners: Development of Robust Evaluation Frameworks: As Agentic AI systems become more autonomous and AGI research advances, standardized
metrics and testing environments [25] will be essential for measuring progress and ensuring reliability. Safety and Governance Mechanisms: The unique risks posed by autonomous systems [50] and potential AGI [38] demand continued investment in alignment research and ethical frameworks.
Practical Implementation Strategies: Organizations should focus on incremental adoption of Agentic AI solutions [34] whilemaintainingrealisticexpectationsaboutAGItimelines. Therapidevolutionofthesetechnologiessuggeststhatwhile AGImayremainyearsordecadesaway,AgenticAIispoised for near-term expansion across industries [20, 29]. By maintainingbalancedperspectivesthatacknowledgeboththe potential and limitations of these paradigms, the AI community can steer development toward beneficial outcomeswhilemitigatingrisks.
Future research should particularly focus on hybrid architectures that combine the strengths of different approaches [21], as well as interdisciplinaryefforts to address the societal implications of increasingly autonomous AI systems [28, 43]
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