Source-linked AI summary
Generative to Agentic AI: Survey, Conceptualization, and Challenges
Johannes Schneider
TL;DR
The paper addresses the limited understanding of how Agentic AI differs from GenAI and what new capabilities, risks, and applications follow. It surveys their characteristics and evolution, then examines reasoning, interaction, specification, evaluation, and challenges. The survey concludes that Agentic AI expands autonomy and interaction but does not guarantee broad performance gains and requires careful specification and responsible deployment.
Problem
The paper addresses the gap in understanding the distinction between GenAI and Agentic AI, including their capabilities, limitations, risks, and novel applications.
Method
The paper synthesizes existing literature to compare GenAI and Agentic AI, trace their evolution, and survey reasoning, interaction, memory, tools, specification, evaluation, and challenges.
Results
The survey finds that Agentic AI introduces reasoning, interaction, and autonomy at a new scale, while performance gains remain task-dependent.
Takeaways & Limitations
Understanding agent capabilities, risks, and specification nuances is essential for advancing research and supporting responsible deployment.
Takeaways & Limitations
Agentic AI does not improve all areas dramatically, and its greater computation can be costly; observability and tool operations may also remain incomplete or opaque.
Abstract
from arXiv · showhide
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.
1 Introduction
Agentic AI extends GenAI with autonomous interaction, tool use, and deeper reasoning for complex tasks, while introducing greater risks and specification challenges. The survey contrasts both paradigms, traces their evolution, and examines applications, limitations, and open challenges.
- Agentic AI versus GenAI: Agentic AI enables systems to act independently, pursue broad objectives, interact with environments and tools, and perform planning and reflection.Its reasoning involves multi-step, problem-dependent computation.
- Agentic AI versus GenAI: Unlike GenAI’s direct input-to-output tasks, Agentic AI uses sequences of tool-mediated actions and feedback to guide subsequent actions.This interaction expands the range of tasks systems can address beyond available input information.
- Opportunities and risks: Agentic AI systems can use less-detailed instructions, autonomous memory, flexible tool selection, and open-ended exploration, increasing both potential benefits and risks.These capabilities make agent specification more demanding than prompting for a narrow task.
- Limitations: Agentic AI does not deliver dramatic improvements in every area and can still make mistakes on relatively simple problems.The survey therefore emphasizes identifying where clear gains enable genuinely novel applications.
- Limitations: Reasoning models show only a slight edge on MMLU at significantly greater computational cost, while domain-specific fine-tuning may perform better and cost less.The passage also notes that benchmark questions may sometimes be answerable through training-data association rather than genuine reasoning.
- Survey scope: The survey compares GenAI and Agentic AI, traces milestones in reasoning and interaction, examines agent specification and evaluation, and develops a future research agenda on challenges and risks.It also discusses overlaps and differences with reinforcement learning and covers single-agent and multi-agent systems.
2 Generative AI to Agentic AI
The paper contrasts GenAI’s direct content generation with Agentic AI’s autonomous reasoning, interaction, and tool use. It traces how Agentic AI addresses selected GenAI limitations while introducing more flexible but demanding computation and system design.
- Defining GenAI and Agentic AI: GenAI typically generates outputs directly from inputs, whereas Agentic AI performs complex reasoning and interacts with environments or tools.Agentic AI combines planning, reflection, and interaction to execute multi-step tasks with less-detailed instructions.
- Defining GenAI and Agentic AI: Agentic AI is conceptualized as foundation-model-based systems that can plan, reflect, use tools, and perform tasks that may not produce digital artifacts.Examples include controlling robots, managing smart grids, and optimizing computational-resource scheduling.
- Evolution toward Agentic AI: The paper presents Agentic AI as an evolution of GenAI, adding tool usage, memory access, reinforcement-learning elements, planning, and interaction.Early GenAI showed basic reasoning and tool-use capabilities, while later systems structured these capabilities more profoundly.
- Why Agentic AI?: Agentic AI can reduce selected GenAI limitations through high-level goal interpretation, larger or dynamically retrieved context, trial-and-error learning, self-verification, and intermediate results.These mechanisms are associated with improved usability, current information access, lower hallucination rates, and easier verification.
- Why Agentic AI?: The survey emphasizes that scaling training data, models, and compute faces limits because domain-specific data can be expensive or impossible to obtain.The paper frames these constraints as motivation for approaches that improve capabilities without anticipating every possible task during training.
- Why Agentic AI?: Agentic AI supports flexible inference-time resource allocation and cost-efficiency configurations, including smaller models that use additional reasoning computation.A single agent can adjust computation based on intermediate outputs, while smaller reasoning models may achieve performance comparable to larger models.
3 Agentic AI
The paper distinguishes Agentic AI from GenAI through three essential areas: reasoning, interaction, and specification of single- and multi-agent systems.
- Reasoning includes problem decomposition, verification, search, and planning.
- Interaction covers engagement with environments, tools, and memory.
- Agentic AI also addresses specification of single-agent and multi-agent systems.
3.1 Reasoning
Agentic AI reasoning extends direct generation through step-by-step computation, decomposition, verification, search, planning, and reflection. The surveyed approaches increasingly organize multiple reasoning paths and intermediate evaluations, while training methods add reasoning-focused data and reinforcement learning.
- Reasoning: Reasoning models generate intermediate results for self-defined subproblems before producing a task solution.For 7 * 12, decomposition can split the calculation into smaller products and then combine them.
- Decomposition: Chain-of-thought prompting elicits multi-step reasoning by providing examples or explicitly asking models to think step-by-step.Least-to-most prompting instead instructs models to decompose and solve subtasks sequentially.
- Planning: Planning searches for action sequences that accomplish goals, with recent systems using planning tools, Monte Carlo Tree Search, or heuristic evaluation.
- Search and verification: Tree, Forest, and Graph of Thoughts generate multiple reasoning paths that are assessed and filtered to improve outcomes.These approaches address the computational expense of evaluating complete chains of thought.
- Verification: Intermediate solutions can be assessed through self-consistency voting, LLM evaluators, human evaluators, or problem-specific heuristics.The appropriate assessment method may depend on the task because partial-solution correctness is often unknown.
- Learning to reason: Reasoning-focused training data and reinforcement learning support increasingly specialized reasoning capabilities.DeepSeekMath curated mathematics-relevant web data, while DeepSeek-R1 used CoT examples and GRPO with accuracy, formatting, and language-consistency rewards.
3.2 Memory
Agentic AI systems use multiple memory forms to manage prompts, learned knowledge, retrieved information, and experience. These designs balance context limits, retrieval relevance, learning needs, and generation cost.
- Memory types: Parametric memory is generally fixed because online updates risk catastrophic forgetting and malicious manipulation.Examples include the Tay chatbot and BlenderBot 3.
- Memory types: Memory includes parametric, ephemeral, retrieval-based, and architectural forms, located internally, externally, or in hybrid model mechanisms.Parametric and ephemeral memory are internal; retrieval-based memory is external; architectural memory is hybrid.
- Memory types: Ephemeral memory is bounded by the context window, whereas retrieval-based memory offers virtually unlimited capacity but makes relevant-information selection difficult.Modern models may hold millions of tokens, but retrieval can include irrelevant content that reduces efficiency.
- Retrieval and context: Retrieval augmentation selects relevant subsets from large knowledge sources, reducing hallucinations and enabling dynamic learning from post-training, private, or experiential data.Vector databases can store document snippets or agent experiences, which may require further processing to extract abstract insights.
- Retrieval and context: Including entire information in the context can outperform subset retrieval, but it increases generation costs and motivates dynamic retrieval decisions.Large-context approaches trade retrieval complexity for higher input and generation costs.
3.3 Tools
Tools extend agents beyond direct text generation by improving accuracy, enabling external interaction, and supporting task-specific functionality. Effective tool use requires selecting, invoking, and sometimes creating tools appropriately.
- Tool functions: Tools reduce errors and improve efficiency while enabling capabilities such as calculation, browsing, and online shopping.They also provide functionality, interaction, interpretability, and control beyond the model alone.
- Tool creation: LLMs can create tools such as Python utilities, scripts, or interfaces to academic-paper code.Some systems propose tools from examples, verify them with unit tests, and wrap them with documentation.
- Tool selection: Selecting among multiple tools is an integral part of planning because different tools support different solution strategies.Approaches include chain-of-thought adaptation, self-supervised invocation timing, and ReAct-style reasoning with action.
- Tool invocation: Fine-tuned LLMs can generate API calls and parse responses from calculators, translation systems, search engines, and out-of-distribution APIs.Subsequent work improves API-call accuracy through fine-tuning and prompting.
3.4 Interacting
Agentic AI interacts with environments through perception, reasoning, and action, extending beyond GenAI’s direct input-to-output generation. Applications span browsers, computers, simulated worlds, robotics, games, and scientific workflows, but substantial limitations remain.
- 3.4.1 Reinforcement Learning: RL agents learn world models and policies from environmental states, transitions, actions, experiences, and feedback.Exploration gathers new information, while exploitation applies existing knowledge toward goals.
- 3.4.1 Reinforcement Learning: Agentic AI incorporates RL concepts but addresses broader, more open-ended tasks than RL agents optimizing narrow reward functions in specific environments.There is no one-to-one correspondence between RL agents and Agentic AI systems.
- 3.4.2 Interacting in (Virtual) Worlds: Agent interaction extends from computer control through mouse, keyboard, and screenshots to multimodal world models and vision-language-action systems.These systems infer or predict actions and states from images, text, and observed trajectories.
- 3.4.2 Interacting in (Virtual) Worlds: Current approaches still struggle with human-intuitive capabilities such as 3D spatial reasoning without extensive task-specific training.LLM-based motion planning can represent scenes textually using object coordinates and output motion trajectories.
- 3.4.2 Interacting in (Virtual) Worlds: Agentic systems support applications including game playing, scientific discovery, human assistance, law, finance, education, medicine, and military workflows.The AI Scientist is described as generating hundreds of medium-quality papers within a week.
- 3.4.2 Interacting in (Virtual) Worlds: Web browsers can function as both tools and environments, enabling agents to access services and perform interactive tasks.On Workarena++, humans score close to 100%, while GPT-4o and Llama3 score close to 0%.
- 3.4.2 Interacting in (Virtual) Worlds: Specialized agent-friendly interfaces can improve success rates compared with controlling general-purpose computer software.The cited evidence demonstrates this improvement for software engineering.
- 3.4.2 Interacting in (Virtual) Worlds: Complex tools may be better conceptualized through reinforcement learning, whereas simpler API usage can rely on documentation, examples, and immediate feedback.The paper treats memory and tool usage as integral Agentic-system components despite their differing interaction patterns.
3.5 Specifying and Evaluating Agentic AI
Agentic AI requires explicit specification of identities, goals, permissions, workflows, interactions, and autonomy, followed by evaluation across complex behaviors. Multi-agent systems add design dimensions involving agent diversity, cooperation, communication, learning, and organization.
- 3.5.1 Specifying Agentic AI: Agent specification must address conformity with system requirements and broader goals such as legal compliance and efficiency.This makes specification a central design concern rather than only a descriptive exercise.
- 3.5.1 Specifying Agentic AI: Agent definitions may include identity, motivation, professional role, tool permissions, delegation rights, workflows, and interaction behavior.In some multi-agent programming settings, a role, goal, and constraints can suffice; partial autonomy may require human consent checkpoints.
- 3.5.1 Specifying Agentic AI: Automatic agent design searches a specified space using an algorithm and evaluation function to assess candidate systems.The approach may fit data or optimize models according to an evaluation function.
- 3.5.2 Multi-Agent Systems: Multi-agent systems define agents and tasks independently, allowing flexible assignment and procedural guidance for agent interactions.Agents may carry roles, goals, personas, tool and delegation permissions, while tasks specify outputs and interaction procedures.
- 3.5.2 Multi-Agent Systems: Multi-agent architectures depend on communication patterns and self-organization strategies, including distributed organization and symmetry breaking.These systems form distributed architectures rather than merely parallel copies of one agent.
- 3.5.2 Multi-Agent Systems: Key multi-agent dimensions include homogeneous versus heterogeneous agents, cooperative versus non-cooperative goals, communication structure, learning, and communication content.Communication commonly packages instructions, goals, state descriptions, action histories, and dialogue histories.
- 3.5.3 Evaluating Agentic AI: Evaluating Agentic AI is difficult because agents are non-deterministic, interact complexly, take longer to complete tasks, and exhibit diverse capabilities.Web benchmarks commonly assess completion of basic human tasks in web environments.
4 Challenges Toward AGI
Agentic AI introduces challenges that become more pronounced as systems reason, interact, use tools, and pursue goals autonomously. These challenges constrain reliability, observability, safety, evaluation, resource management, and progress toward AGI.
- Agentic AI challenges can hinder further evolution toward Artificial General Intelligence while also creating research opportunities.
- Errors: Longer, more complex agent tasks increase the risk of cumulative errors across multiple steps, even though reasoning can reduce errors compared with GenAI.
- Interpretability: Agentic AI is more complex than GenAI because it integrates planning, external memory, and other components, while chain-of-thought may not faithfully reflect decisions.
- Dynamic and Complex Environments: Agents operate in dynamic environments with changing conditions, tool selection, and sometimes parallel collaboration with other agents.
- Observability: Partial observability and opaque APIs can prevent agents’ environments, behaviors, and tool operations from being fully transparent.
- Safety and Security: Agentic AI faces increased safety and security risks because greater interaction in less controlled environments exposes capabilities such as tool invocation to exploitation.
- Evaluation and Control: Evaluating reliability, task performance, and potential harms remains challenging and incomplete, while human approvals may have difficult-to-anticipate consequences.
- Resource Allocation and Management: Dynamic agents may consume varying and potentially arbitrary amounts of information, making computational resource management more demanding.
5 Methodology and Related Work
The survey adopts a high-level, capability-focused approach to contrast Agentic AI with GenAI for a broad academic and professional audience. It differentiates itself from prior surveys through this comparison and through expanded conceptual and practical treatment of agents.
- Methodology: The survey targets academics and industry professionals by presenting basic distinctions accessibly while reserving recent innovations for technically focused readers.
- Methodology: The authors compare GenAI and Agentic AI from a high-level capability perspective, assuming familiarity with deep learning, foundation models, and prompt engineering.
- Related Surveys: Prior surveys did not jointly provide this survey’s direct GenAI–Agentic AI distinction, multi-angled agent definition, detailed autonomy discussion, and AGI contrast.
- Related Surveys: The survey expands practical coverage of agent definition and memory characteristics while offering a different conceptual framework.
- Related Surveys: Compared with Wang et al., the survey is capability-focused rather than architecture-centric and emphasizes distinguishing GenAI from Agentic AI.
- Related Surveys: Compared with Xi et al., the survey emphasizes reasoning and interaction rather than brain, perception, and action, while retaining its GenAI–Agentic AI distinction.
- Related Surveys: Compared with Acharya et al., the survey begins with GenAI for readers familiar with systems such as ChatGPT rather than covering AI’s entire history.
- Related Surveys: Compared with Plaat et al. and Cheng et al., the survey differs in its capability framing, two-capability emphasis, and treatment of retrieval augmentation as tool interaction.
6 Conclusion
The conclusion characterizes Agentic AI as a major extension beyond GenAI and emphasizes the need to understand its capabilities, risks, and specification challenges. The survey connects technical foundations with practical deployment concerns and open research questions.
- Agentic AI extends beyond GenAI by introducing reasoning, interaction, and autonomy at a new scale.
- The survey systematically contrasts Agentic AI and GenAI while covering technical foundations, practical specification, and open challenges.
- Understanding agent capabilities, risks, and specification nuances is presented as essential for advancing research and ensuring responsible deployment.
Declarations
The paper reports no conflicts of interest, no funding for the study, and no applicable data-sharing materials because no datasets were generated or analyzed.
- The authors declare that they have no conflict of interest.
- No funding was received for conducting this study.
- Data sharing is not applicable because no datasets were generated or analyzed, and all references are included in the paper.