AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges

In plain words

An AI agent is a language model wired to tools, such as a search engine, a calendar or an email client. It is given one job, works through it step by step, and reports back. The paper says this is the simpler of two designs.

Agentic AI is the bigger design. Several specialized agents share a goal. One splits the goal into parts, others handle the parts, they share a memory and a coordinator keeps them on track. The authors argue that treating the two as the same thing leads to choosing the wrong design for a problem.

The paper walks through how each design is built, where each is used today, what goes wrong with each, and which fixes are suggested. It is a review of the literature, not a new system, and it does not test either design itself.

TL;DR

• The paper defines AI Agents as LLM-driven modules for narrow tasks, and Agentic AI as multi-agent systems with task decomposition, persistent memory and coordinated autonomy § abstract p. 1.
• It contrasts Generative AI, AI Agent, Agentic AI and a "Generative Agent" category it labels as inferred tab. 2 p. 10.
• Nothing is measured. All comparisons are qualitative tables and cited examples.
• Useful as a vocabulary and a checklist of failure modes. Do not treat its tables as benchmark evidence.

Problem

The authors say existing surveys often merge AI Agents and Agentic AI under one heading, which blurs system design, deployment and evaluation choices §1.1 p. 3. They also note that much recent literature covers either tool-using LLMs or multi-agent systems, rarely both together §1.1 p. 3.

The paper's stated gap is the lack of a structured taxonomy that separates the two paradigms along autonomy, coordination, interaction and reasoning scope §1.1 p. 3.

Method

This is a narrative literature review. It does not present an algorithm, a model or an experiment.

1. Literature search. The authors searched academic databases and AI-powered discovery tools, twelve platforms in total, with Boolean query strings such as "AI Agents", "Agentic AI", "LLM Agents" and "Multi-Agent AI Systems" §1.1.1 p. 4. The inclusion criteria named are novelty, empirical evaluation, architectural contribution and citation impact §1.1.1 p. 4. No counts of records screened or included are given §1.1.1 p. 4.
2. Foundations. AI Agents are described by three traits: autonomy, task-specificity and reactivity §2.0.1 p. 4 fig. 4 p. 5. The paper then covers LLMs and LIMs as their reasoning and perception engines §2.0.2 p. 5 and Generative AI as a precursor §2.0.3 p. 6.
3. Tool-augmented agents. The agent cycle is described as tool invocation, then result integration into the model's context, then the next step §2.1.2 [pp. 6–7] fig. 6 p. 7.
4. Taxonomy. Four paradigms are compared on five dimensions, each in its own table: core function and goal tab. 5 p. 11, architecture tab. 6 p. 11, operational mechanism tab. 7 p. 11, scope and complexity tab. 8 p. 12, and interaction and autonomy tab. 9 p. 12. A conceptual table covers initiation, goal flexibility, temporal continuity, learning, memory, coordination and key role tab. 2 p. 10.
5. AI Agent architecture. Four modules: perception, knowledge representation and reasoning, action selection and execution, and basic learning and adaptation §3.1.1 p. 10.
6. Agentic AI architecture. The paper adds four enhancements to the AI Agent base: an ensemble of specialized agents, advanced reasoning and planning, persistent memory, and orchestration layers or meta-agents §3.1.2 [pp. 12–13].
7. Applications. Four application areas per paradigm: customer support and internal enterprise search, email filtering, personalized content and data reporting, and scheduling assistants for AI Agents §4.0.1 [pp. 13–15] fig. 9 p. 14. For Agentic AI: multi-agent research assistants, intelligent robotics coordination, collaborative medical decision support, and multi-agent game AI with adaptive workflow automation §4.0.2 [pp. 15–17].
8. Challenges. AI Agent limitations: causal understanding, inherited LLM limits, incomplete agentic properties, long-horizon planning, reliability and safety §5.0.1 [pp. 16–19] fig. 12a p. 18. Agentic AI challenges: amplified causality, communication bottlenecks, emergent behavior, scalability and debugging, trust and verification, security, ethics and governance, and immature foundations §5.0.2 [pp. 19–21] fig. 12b p. 18.
9. Solutions. Ten mechanisms: retrieval-augmented generation, tool-augmented reasoning, the reason-act-observe loop, memory architectures, role-specialized orchestration, reflexive self-critique, programmatic prompt pipelines, causal modeling with simulation-based planning, monitoring and auditing pipelines, and governance-aware architectures §6.1 [pp. 22–24] fig. 13 p. 23.
10. Roadmap. Proactive intelligence, tool integration, causal reasoning, continual learning and trust and safety for AI Agents. Multi-agent scaling, unified orchestration, persistent memory, simulation planning, ethical governance and domain-specific systems for Agentic AI §6.2 [pp. 24–25] fig. 14 p. 25.

The paper defines the two taxonomies in prose and tables only. It has no equations and no algorithm boxes.

Agent loop as described in the paper. This is a prose description, not an algorithm the authors specify:

# Agent loop: reason, act, observe  [§6.1] [p. 22] [§2.1.2] [p. 6]
# Input:  goal (text), tools (name -> callable)
# Output: answer (text)

def run_agent(goal, tools):
    # Keep the goal, the calls and their results in one context  [§2.1.2] [p. 6]
    context = [goal]
    # Stop rule and step budget: not stated [p. 22]
    while True:
        # The model reasons, then emits a tool call or a final answer  [§2.1.2]
        step = llm_decide(context)
        if step.is_final_answer:
            return step.text
        # Call the tool with structured arguments, e.g. JSON  [§2.1.2] [p. 6]
        result = tools[step.tool_name](**step.arguments)
        # Feed the observation back so the next step can use it  [§6.1] [p. 22]
        context.append(result)

Hyperparameters

Not applicable. The paper reports no models trained, no runs and no settings §1.1 p. 3.

Results

This is a review. It has no experiments and no benchmark numbers §1.1 p. 3. The counts below are the paper's own structural outputs, not measured results, each cited to the section that defines it §4 [pp. 13–17] §6.1 [pp. 22–24].

The paper's headline conclusion is that AI Agents are task-specific, single-entity systems with limited autonomy and reactivity, while Agentic AI is a multi-agent paradigm with distributed planning, shared memory and orchestration §7 p. 25.

Paper vs code

No official code. The arXiv metadata has no code link. The paper's only repository links point to third-party projects (BabyAGI and AutoGPT) §1 p. 2, not to the authors. I searched for a project page (none is linked in the paper) and ran a GitHub repository search for the exact title and for the author names (2026-10-18); both returned zero repositories.

Pitfalls

• Version. The source is the journal PDF, Information Fusion 126 (2026), article 103599 p. 1. The metadata records arXiv v5, updated 2025-09-30 [raw metadata]. The arXiv version's LaTeX source is not available here, so page numbers refer to the PDF.
• Not a method paper. There is nothing to implement from the paper alone. The AI Agent versus Agentic AI split is presented as "a widely accepted conceptual illustration" §3.0.1 p. 7, but the paper cites no formal definition for it. Treat the boundary as a useful convention, not a standard. "Generative Agent" is labelled "inferred" tab. 2 p. 10, which is the authors' own category.
• Internal inconsistencies. Check these before quoting the paper:
  • Learning. AI Agents "learn and adapt within their specific domain" tab. 1 p. 9, but tab. 2 says "(Might in future)" tab. 2 p. 10 and tab. 3 says "rule-based or supervised" tab. 3 p. 10.
  • Autonomy. Tab. 1 rates AI Agent autonomy "high" tab. 1 p. 9; tab. 9 rates it "medium" tab. 9 p. 12.
  • Roadmap. The text says "five domains" §6.2 p. 24. Fig. 14 shows five branches for AI Agents and six for Agentic AI fig. 14 p. 25.
  • Challenges. Fig. 12a shows four AI Agent limitations fig. 12 p. 18; the text lists five §5.0.1 [pp. 16–19].
  • Cross-reference. §3.0.2 cites tab. 10 as a source for the paradigm distinctions §3.0.2 p. 8. Tab. 10 is a list of commercial systems, so this looks like a wrong table number.
• Ratings are unscored. The High, Medium and Low labels in tabs. 2 to 9 come with no scoring rubric and no cited measurement. Do not convert them into numbers.
• Examples are illustrative. Figs. 5, 7, 9, 10 and 11 are illustrations, and the authors note that ChatGPT-4o was used to generate realistic visualizations [Statement on AI writing assistance] p. 25. The scenarios in §4 (for example, an NSF proposal drafted by a multi-agent team §4.0.2 p. 15 and an executive scheduling assistant §4.0.1 p. 14) are presented as hypothetical or illustrative, not as reported deployments. One sentence says early ICU deployments "demonstrated increased diagnostic precision" §4.0.2 p. 16 without naming a study in that sentence.
• Tables 10 and 11 are a dated snapshot. Each row gives a product or project name and a one-line description, with a "Source Link" tab. 10 p. 18 tab. 11 p. 19. There is no access date and no evaluation. Several product claims may have changed since mid-2025.
• Time-sensitive facts. The paper mentions GPT-4.1 context windows up to one million tokens §2.0.3 p. 6 and Google's A2A protocol introduced in 2025 §1 p. 2. Check these against current documentation before relying on them.
• Terminology. "LIMs" (large image models) is used for CLIP and BLIP-2 §2.0.2 p. 5. It is not a standard term; these are vision-language models. The abstract uses it too p. 1.
• Nothing specifies the loop. The paper describes the reason-act-observe cycle in prose §6.1 p. 22 but gives no stop rule, no step budget, no memory schema and no orchestrator policy. Any implementation has to choose these; the paper does not say what to choose.
• Search is not reproducible. Platforms and query strings are listed §1.1.1 p. 4, but no counts of records screened or included are reported. The inclusion criteria (novelty, empirical evaluation, citation impact) are not applied in any visible table.

Lineage

• Builds on: the AI agent term from Castelfranchi's 1998 work §1 p. 2 and the multi-agent systems literature §1 [pp. 1–2]. The paper does not present its own method on top of these.
• Compared against: the paper's own four paradigms (Generative AI, AI Agent, Agentic AI, Generative Agent) across tabs. 2 to 9 §3.0.2 [pp. 8–12].
• Superseded by: not stated.