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Both paradigms build on top of LLMs and push the boundaries of generative systems - but they follow very different architectures, levels of autonomy, and coordination models.


This recent paper "AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges" proposes a structured taxonomy to clarify these distinctions and align the terminology.
Here's a quick breakdown:


🔍 What they have in common:

- Both use LLMs
- Both extend generative capabilities


⚡ How they differ:

- AI Agents: Single-entity systems built for goal-directed tasks, often using tools and step-by-step reasoning
- Agentic AI Systems: Multi-agent setups that collaborate, delegate, and dynamically coordinate subtasks across workflows


👉 Why this distinction matters:

1. Better system design:
- Use AI Agents for modular, tool-assisted tasks
- Use Agentic AI for orchestrated, multi-agent operations

2. More accurate benchmarking:
- Performance, safety, and resource needs differ widely between the two

3. Avoid design errors:
- Prevent assumptions like inter-agent coordination where it's not designed to happen


👉 Key challenges for both paradigms

- Hallucination
- Shallow reasoning
- Lack of causality
- Scalability
- Governance risks


🛠 Emerging solutions include:

- RAG (retrieval-augmented generation)
- Tool-based reasoning
- Memory systems
-Simulation-based planning


👉 If you're building or researching with AI agents, this paper is a must-read to clarify your architecture and avoid conceptual traps.

Paper link: https://arxiv.org/pdf/2505.10468

Link to the LinkedIn post: 🧠 AI Agents vs Agentic AI - what's the difference, and why does it... | Hanane D. | 29 comments