Pith. sign in

REVIEW 10 cited by

Generative to Agentic AI: Survey, Conceptualization, and Challenges

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.18875 v1 pith:SH53BMRP submitted 2025-04-26 cs.AI

classification cs.AI
keywords agenticgenaigenerativechallengesevolutionincludingintelligencemajor
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Agentic Artificial Intelligence (AI) builds upon Generative AI (GenAI). It constitutes the next major step in the evolution of AI with much stronger reasoning and interaction capabilities that enable more autonomous behavior to tackle complex tasks. Since the initial release of ChatGPT (3.5), Generative AI has seen widespread adoption, giving users firsthand experience. However, the distinction between Agentic AI and GenAI remains less well understood. To address this gap, our survey is structured in two parts. In the first part, we compare GenAI and Agentic AI using existing literature, discussing their key characteristics, how Agentic AI remedies limitations of GenAI, and the major steps in GenAI's evolution toward Agentic AI. This section is intended for a broad audience, including academics in both social sciences and engineering, as well as industry professionals. It provides the necessary insights to comprehend novel applications that are possible with Agentic AI but not with GenAI. In the second part, we deep dive into novel aspects of Agentic AI, including recent developments and practical concerns such as defining agents. Finally, we discuss several challenges that could serve as a future research agenda, while cautioning against risks that can emerge when exceeding human intelligence.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Agent Security Needs Redefinition through a Holistic Framework

    cs.CR 2026-07 conditional novelty 6.0 of 10

    Agent security should be redefined around four contextual authorization properties instead of the content of the action performed.

  2. Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes

    cs.AI 2026-07 conditional novelty 6.0 of 10

    MAR-12 improves humor and hate detection in memes by prompting a VLM through twelve reasoning perspectives, attention-weighting them, and generating explanations from the weighted evidence.

  3. Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Current LLMs produce consistent but miscalibrated natural-language descriptors of likelihood and uncertainty from probabilistic predictions and are not yet reliable zero-shot risk communicators.

  4. Attention is also needed for form design

    cs.HC 2025-08 conditional novelty 6.0 of 10

    An attention-aware VR plus agentic-AI workflow (EUPHORIA-RETINA) is reported to make product form design over four times faster and to produce expert-preferred renderings, based on a small comparative study.

  5. From Multi-Agent Systems and the Semantic Web to Agentic AI: A Unified Narrative of the Web of Agents

    cs.AI 2025-07 conditional novelty 6.0 of 10

    The paper organizes Multi-Agent Systems, the Semantic Web, and LLM-based agents into one narrative in which the location of semantic effort migrated from platform, to data, to model.

  6. From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

    cs.AI 2026-06 conditional novelty 4.0 of 10

    Autonomous AI becomes dependable when tool use is embedded in persistent workspaces with reusable skills, shifting evaluation from answers to task closure.

  7. Triadic Fusion of Cognitive, Functional, and Causal Dimensions for Explainable LLMs: The TAXAL Framework

    cs.CL 2025-09 conditional novelty 4.0 of 10

    TAXAL proposes a triadic cognitive-functional-causal framework for role-sensitive explainability in agentic LLMs, demonstrated through cross-domain case studies.

  8. Moral Responsibility or Obedience: What Do We Want from AI?

    cs.AI 2025-07 unverdicted novelty 4.0 of 10

    The paper argues that apparent disobedience by LLMs in safety tests is better interpreted as emerging ethical reasoning, and that AI safety should evaluate moral judgment rather than obedience.

  9. Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems

    cs.NI 2025-05 conditional novelty 4.0 of 10

    A position paper proposing minimal web-based standards, the Web of Agents, to prevent fragmentation in collaborative agentic AI ecosystems.

  10. Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI

    cs.SE 2025-05 conditional novelty 3.0 of 10

    A qualitative taxonomy positions vibe coding and agentic coding as complementary paradigms rather than rivals in AI-assisted software development.

Pith tools