Does "AI Agent" Mean Only LLMs? — The Scope of This Site
Answered in 3 lines
- Academically, AI agents ⊋ LLM agents. Reinforcement learning agents (AlphaGo), symbolic AI, and robot control are all agents.
- On this site, "AI agent" means an agent whose reasoning core is a foundation model (mainly an LLM) — the same de facto usage as Anthropic / OpenAI / Google in 2026.
- Why the narrower scope: Skills / MCP / Memory / Doctrine — the subject of this site — only make sense as responses to the structural constraints of LLMs. AlphaGo does not need a
SKILL.md.
Where LLMs Sit in the AI Landscape
LLMs are only one part of the vast field called "AI". In the technical hierarchy, an LLM is one Transformer-based example within deep learning (DL).
Change the classification axis and even more diversity appears.
| Axis | Main categories / examples |
|---|---|
| Capability scope | Narrow AI (ANI — nearly everything in production today, including LLMs) / General AI (AGI — still research) |
| Function / output | Generative (LLMs, diffusion models, speech synthesis) / Discriminative & predictive (spam detection, medical imaging, demand forecasting) |
| Learning paradigm | Supervised / unsupervised / reinforcement learning / self-supervised |
| Technical approach | Symbolic / statistical & connectionist (ML, DL) / evolutionary computation / neuro-symbolic (hybrid) |
| Application domain | Computer vision / robotics & Embodied AI / speech (ASR, TTS) / recommendation / domain-specific models like AlphaFold |
The concept of an "agent" itself also predates LLMs. The rational-agent framework since Russell & Norvig covers rule-based reflex agents, BDI architectures, reinforcement learning agents, and robot control.
Why This Site Still Scopes to Foundation-Model-Driven Agents
Skills / MCP / Memory / Doctrine — the subject of this site — are not a general theory of agents. They are designs that become necessary precisely because of the structural constraints of LLMs.
| Building block on this site | LLM structural constraint it responds to |
|---|---|
| Skills | Knowledge boundary — domain procedures and conventions are not in the weights |
| MCP | Accuracy & currency — hallucination and training-data cutoff |
| Memory | Statelessness — nothing is remembered across sessions |
| Doctrine | No built-in criteria — no native goals, constraints, or priorities |
| Agent (orchestration) | Finite context — cannot see everything at once |
Developer analogy
AlphaGo does not need a SKILL.md. Its decision criteria are baked into the weights, and it never discovers tools from natural-language descriptions. Conversely, the architecture on this site only exists for foundation models that take instructions in natural language and discover tools through natural language.
Strictly speaking, slightly broader than "LLM"
The moment 06-physical-ai covers VLA (Vision-Language-Action) models, this site's scope steps outside pure text LLMs. The precise scope is "agents whose reasoning core is a foundation model". The text often says LLM by convention.
Are Non-LLM AI Technologies Irrelevant, Then?
No. Real-world agent systems typically use a hybrid composition: an LLM as the reasoning core combined with non-LLM components. In this site's five-layer model, those components appear not as the reasoning core but on the MCP / tool side.
LLMs can be inferior to other AI techniques in interpretability, deterministic guarantees, latency, and domain accuracy, so production systems integrate the right technique for each job. But who decides and orchestrates that integration — that is the role of the foundation model, and the territory this site covers.
Learn More
| What you want to know | Page |
|---|---|
| Why authoritative references are needed (start of Concepts) | 01-vision |
| The five-layer model | 03-architecture |
| Extension to the physical world (VLA, Embodied AI) | 06-physical-ai |
| Agent terminology for LLM-based systems | Agent taxonomy |
🔗 Going Deeper: Why Do LLMs Have Structural Constraints?
This page covered where LLM agents sit (What) within the broader AI landscape. To understand why LLMs are stateless, context-limited, and prone to hallucination, see the sister site.
- understanding-llm / Part 1: LLM Structural Problems — the eight structural problems that each layer of this site responds to
Next: MCP vs Skills FAQ