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

Key points

• Problem: The authors argue that existing surveys fold AI Agents and Agentic AI into one umbrella of intelligent agents, which blurs the concepts and misaligns system design. Without a clear line, developers under-engineer tasks that need agent coordination and over-engineer tasks a single agent could solve.
• Scope: This is a literature review (survey). The authors searched twelve platforms, academic databases such as Google Scholar, IEEE Xplore and arXiv plus AI tools such as ChatGPT, Perplexity.ai and Grok, with terms like AI Agents and Agentic AI. Papers were included for novelty, empirical evaluation, architectural contribution and citation impact.
• Taxonomy: An AI Agent is a single system that uses LLMs and LIMs (Large Image Models such as CLIP) and calls tools for a narrow task, defined by autonomy, task specificity and reactivity. Agentic AI is a system of specialized agents that split a goal, communicate, and are coordinated through persistent memory and an orchestrator (meta-agent). The comparison tables (Tables 1 to 9) contrast the two types against Generative AI as the baseline and add Generative Agents as an inferred fourth type.
• Applications: For AI Agents the review covers customer support with enterprise search, email triage, personalized recommendation with basic reporting, and scheduling assistants. For Agentic AI it covers multi-agent research assistants, robotics coordination, collaborative medical decision support, and game AI with adaptive workflow automation. Tables 10 and 11 list representative AI Agent and Agentic AI systems from 2023 to 2025 by application area and mode of operation.
• Challenges and remedies: The review names four main limits of AI Agents: missing causal reasoning, hallucination and shallow reasoning inherited from LLMs, incomplete agentic properties such as autonomy and proactivity, and failures in long-horizon planning and recovery. For Agentic AI it names eight bottlenecks, including inter-agent error cascades, coordination breakdowns, emergent instability, scalability limits and poor explainability. As remedies it proposes ten design strategies such as RAG, function calling, ReAct loops, memory architectures, role-based orchestration and causal modeling.
• Limits: The authors themselves cite critiques that agent benchmarks rely on artificial environments and that Agentic AI work favors architectural novelty over rigorous empirical validation. The review rests on literature and conceptual comparison, and its detailed application examples are hypothetical scenarios. For medical decision support, the authors state that real-world implementations are still lacking.

Paper links

External research summaries. These are not HDATF publications or measured product results.