The paper "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges" (Sapkota et al., 15 May 2025 ) provides a detailed review and taxonomy differentiating AI Agents and Agentic AI, tracing their evolution from foundational generative AI models. The authors aim to clarify the distinct design philosophies, capabilities, and challenges of these two paradigms to offer a roadmap for their development and application.
Before the widespread adoption of large generative models around 2022, autonomous agent development was rooted in multi-agent systems (MAS) and expert systems, which focused on rule-based logic, symbolic reasoning, and limited autonomy. These systems were primarily reactive or deliberative within predefined boundaries and lacked the learning-driven, context-aware capabilities of modern agents. The rise of LLMs and large image models (LIMs) since late 2022 has catalyzed a shift towards more autonomous, task-oriented frameworks, leading to the emergence of AI Agents and Agentic AI.
The paper distinguishes between Generative AI, AI Agents, and Agentic AI. Generative AI, like ChatGPT, is seen as a precursor, primarily focused on producing novel content based on prompts. These systems are reactive, stateless, and lack intrinsic feedback loops or multi-step planning.
AI Agents are defined as autonomous software entities designed for goal-directed task execution within bounded digital environments. They perceive inputs, reason over information, and initiate actions. Their core characteristics include:
- Autonomy: Ability to function with minimal human intervention post-deployment.
- Task-Specificity: Optimized for narrow, well-defined tasks like email filtering or scheduling.
- Reactivity and Adaptation: Capacity to respond to dynamic inputs, potentially with basic learning heuristics or feedback loops.
LLMs and LIMs serve as the core reasoning and perception engines for modern AI Agents, enabling them to understand natural language, interpret visual inputs, plan multi-step solutions, and interact with environments. Tool-augmented AI Agents enhance LLMs with capabilities for external tool use, function calling, and sequential reasoning (e.g., using frameworks like LangChain or AutoGPT). This allows them to access real-time information, execute code, and interact with dynamic data environments, overcoming the limitations of static, generative-only systems. Examples of AI Agent applications include customer support automation, internal enterprise search, email filtering, personalized recommendations, basic data reporting, and autonomous scheduling assistants.
Agentic AI represents a significant conceptual leap, moving from isolated AI Agents to collaborative, multi-agent systems. These systems are composed of multiple, specialized agents that coordinate, communicate, and dynamically allocate sub-tasks to achieve complex, high-level objectives. Key enablers include goal decomposition (parsing objectives into smaller tasks), multi-step reasoning, inter-agent communication (via channels like message queues or shared memory), and reflective reasoning/memory systems (episodic, semantic, vector memory).
The architectural evolution from AI Agents to Agentic AI involves enhancing core perception, reasoning, and action modules with advanced components like:
- Ensemble of Specialized Agents: Multiple agents, each with a distinct function, interacting collaboratively.
- Advanced Reasoning and Planning: Recursive reasoning using techniques like ReAct, Chain-of-Thought (CoT), and Tree of Thoughts for dynamic adaptation.
- Persistent Memory Architectures: Systems to maintain context and knowledge across task cycles or agent sessions.
- Orchestration Layers / Meta-Agents: Entities that coordinate subordinate agents, manage dependencies, and resolve conflicts (e.g., in frameworks like AutoGen or MetaGPT).
Agentic AI systems are applied to broader and more dynamic scenarios compared to single AI Agents. Applications include multi-agent research assistants (collaboratively retrieving, synthesizing, and drafting content), intelligent robotics coordination (e.g., coordinated drone swarms or multi-robot harvesting), collaborative medical decision support (agents handling diagnostics, monitoring, and treatment planning), and multi-agent game AI/adaptive workflow automation.
Despite their potential, both paradigms face significant challenges and limitations. Challenges for AI Agents:
- Lack of Causal Understanding: Relying on statistical correlation rather than cause-effect reasoning makes them brittle under distributional shifts.
- Inherited Limitations from LLMs: Susceptibility to hallucinations, prompt sensitivity, shallow reasoning, high computational cost/latency, static knowledge cutoffs, and bias reproduction.
- Incomplete Agentic Properties: Often exhibit only partial autonomy, lack proactivity, have constrained reactivity, and limited social ability compared to canonical agent definitions.
- Limited Long-Horizon Planning and Recovery: Struggle with complex, multi-stage tasks and lack robust error detection or recovery mechanisms.
- Reliability and Safety Concerns: Not yet verifiable or safe enough for critical applications.
Challenges for Agentic AI:
- Amplified Causality Challenges: Inter-agent dynamics compound the lack of causal reasoning, leading to coordination breakdowns and error cascades.
- Communication and Coordination Bottlenecks: Difficulties in goal alignment, limited communication protocols, and resource contention among agents.
- Emergent Behavior and Predictability: Complex interactions can lead to unpredictable, unintended, or unstable system-level behaviors.
- Scalability and Debugging Complexity: Black-box reasoning chains and non-compositionality make large-scale systems hard to debug and maintain.
- Trust, Explainability, and Verification: Distributed architecture increases opacity, making it hard to trace decisions or formally verify behavior.
- Security and Adversarial Risks: Expanded attack surface with potential for single points of compromise and exploitation of inter-agent dynamics.
- Ethical and Governance Challenges: Ambiguity in accountability, potential for amplified biases, and misalignment of individual agent goals with human intent.
- Immature Foundations and Research Gaps: Lack of standard architectures, unresolved causal inference issues, and need for robust benchmarks.
The paper outlines potential solutions and a future roadmap for both AI Agents and Agentic AI. Solutions for mitigating challenges include Retrieval-Augmented Generation (RAG) for grounding, tool-augmented reasoning via function calling, iterative agentic loops (ReAct), advanced memory architectures, multi-agent orchestration with role specialization, reflexive and self-critique mechanisms, programmatic prompt engineering, causal modeling and simulation, monitoring/auditing/explainability pipelines, and governance-aware architectures.
The future roadmap for AI Agents envisions a focus on proactive intelligence, enhanced tool integration, deeper causal reasoning, continuous learning from interactions, and built-in trust and safety mechanisms. For Agentic AI, the roadmap includes multi-agent scaling, unified orchestration frameworks, sophisticated persistent memory, simulation-based planning, robust ethical governance, and the development of domain-specific systems tailored for complex applications.
In conclusion, the paper provides a valuable taxonomy for understanding the current landscape of AI Agents and Agentic AI, differentiating them based on architecture, operational mechanisms, scope, and autonomy. It highlights the critical role of foundational models and the architectural evolution necessary for multi-agent collaboration while comprehensively detailing the challenges that must be addressed to achieve robust, scalable, and trustworthy autonomous systems.