9 LLM Architectures Powering AI Agents — And When to Use Each One
Photo by Steve Johnson on Unsplash
AI agents are everywhere — coding assistants, autonomous research tools, self-driving workflows. But under the hood, they're not all running the same type of model. Different tasks require different architectures, and understanding them is becoming essential for any developer building with AI.
Here are the 9 LLM architectures that power modern AI agents, what each one does, and when you'd pick one over another.
The 9 Architectures
LLM — Large Language Models
LLMs are deep neural networks trained on massive text datasets to understand and generate human language. They form the foundation layer for virtually every modern AI agent.
How they work: Tokenize input, embed tokens into vectors, process through transformer attention layers, model context, retrieve knowledge, predict next tokens, generate sequences, produce output.
Examples: GPT-4, Claude (Anthropic), Gemini (Google), Llama (Meta)
GPT — General Pretrained Transformer
GPT models generate text by predicting the next token based on context. They are pretrained on massive datasets and fine-tuned for different applications. This is the architecture that started the current AI revolution.
How they work: Tokenize input, encode tokens, process through transformer layers, apply pretrained knowledge, predict next token, generate sequence, produce output.
Used in: Chatbots, content generation, coding assistants, AI copilots
Key insight: GPT is autoregressive — it generates one token at a time, each informed by all previous tokens. This is why longer contexts produce better outputs, and why context window size matters.
SLM — Small Language Models
SLMs are compact versions of LLMs designed for speed, efficiency, and local deployment. They require less compute and are ideal for edge devices and real-time applications where you can't afford cloud API latency.
How they work: Same transformer architecture as LLMs, but with fewer parameters, compact layers, and efficient attention mechanisms. They trade some capability for dramatically faster inference.
Used in: Mobile AI assistants, embedded AI systems, edge AI devices
Examples: Phi-3 (Microsoft), Gemma (Google), Llama 3.2 1B/3B, Qwen 2.5 0.5B
MoE — Mixture of Experts
MoE models route each input to specialized sub-models called "experts." A gating network decides which expert handles each task, allowing the system to scale efficiently without activating all parameters for every request.
How they work: Tokenize input, gating network makes routing decision, route to selected experts, experts process independently, select top experts, merge outputs, generate response, output.
Used in: Many large frontier models (GPT-4 is widely believed to use MoE, Mixtral by Mistral is confirmed MoE)
Why it's clever: A 1.8 trillion parameter MoE model might only activate 200 billion parameters per query. You get the capability of a massive model with the inference cost of a much smaller one. It's how companies build "smart" models that are still fast.
VLM — Vision Language Models
VLMs process both images and text. They combine visual understanding with language reasoning to interpret images, documents, screenshots, or video.
How they work: Image encoding (via vision encoder like ViT) + text encoding, multimodal fusion to create joint representation, context reasoning across both modalities, generate text response about the visual input.
Used in: Image captioning, visual search, document AI, robotics perception, screenshot understanding
Examples: GPT-4o (OpenAI), Claude 3.5 Sonnet (Anthropic), Gemini Pro Vision (Google), LLaVA (open source)
Photo by Andrea De Santis on Unsplash
LRM — Large Reasoning Models
LRMs are optimized for multi-step reasoning and complex problem solving. They break problems into smaller parts and reason through intermediate steps before producing answers — similar to how a human would work through a math proof.
How they work: Decompose problem into sub-problems, generate reasoning steps (chain-of-thought), evaluate intermediate states, refine reasoning, verify results, construct final answer.
Used in: Math reasoning, scientific reasoning, complex planning tasks, code debugging
Examples: o1/o3 (OpenAI), Claude with extended thinking, DeepSeek-R1
Key difference from LLMs: Standard LLMs generate answers directly. LRMs spend compute on thinking before answering. This "thinking time" dramatically improves accuracy on hard problems but makes simple queries slower and more expensive.
LAM — Large Action Models
LAMs are designed to take actions in the real world — not just generate text. They understand user intent and execute tasks by interacting with apps, APIs, and operating systems.
How they work: Parse user intent, plan action sequence, interact with tools/APIs/UI elements, execute actions step by step, verify results, report back.
Used in: Browser automation, app control, workflow orchestration, robotic process automation
Examples: Claude Code (tool use), Rabbit R1 (LAM), computer-use agents, Devin (software engineering agent)
HRM — Hierarchical Reasoning Model
HRMs organize reasoning across multiple layers of abstraction. High-level planning handles strategy, while lower-level layers perform faster computation. This improves efficiency for complex tasks that need both big-picture thinking and detailed execution.
How they work: High-level planning (strategy), low-level computation (execution), iterative updates between layers, feedback loops for refinement, hierarchical convergence, decode results.
Think of it like: A CEO (high-level reasoning) sets the strategy, managers (mid-level) translate to tasks, individual contributors (low-level) execute. Each level operates at different speeds and abstraction levels.
Used in: Complex multi-step planning, autonomous systems, research agents that need to both plan and execute
LCM — Large Concept Models
LCMs focus on conceptual understanding rather than token prediction. Instead of generating text word-by-word, they map semantic concepts and relationships across knowledge spaces. This improves deep reasoning and knowledge representation.
How they work: Normalize representations into concept space, diffusion refinement, concept interaction mapping, semantic mapping across knowledge domains, decode concepts back into language, generate response.
Why they matter: Standard LLMs can sometimes produce fluent text that's factually wrong — they're great at language patterns but weaker at actual understanding. LCMs try to model the meaning behind text, not just the statistical patterns.
Example: Meta's Large Concept Model (2024) — operates on sentence-level semantic representations rather than individual tokens.
How Modern AI Agents Combine These
The most capable AI agents don't use just one architecture — they combine several:
- LLM for language understanding
- LRM for complex reasoning
- LAM for tool execution and actions
- VLM for multimodal perception
- MoE for scalable compute
This combination creates Agentic AI systems capable of reasoning, planning, and acting — not just chatting.
Quick Reference
| Architecture | Strength | Best For | Example |
|---|---|---|---|
| LLM | Language understanding | General AI tasks | Claude, GPT-4 |
| GPT | Text generation | Chatbots, coding | GPT-4, Llama |
| SLM | Speed, efficiency | Edge/mobile devices | Phi-3, Gemma |
| MoE | Scalable compute | Large-scale inference | Mixtral, GPT-4 |
| VLM | Image + text | Visual understanding | GPT-4o, Claude |
| LRM | Deep reasoning | Math, science, logic | o1, DeepSeek-R1 |
| LAM | Taking actions | Automation, agents | Claude Code, Devin |
| HRM | Multi-level planning | Complex workflows | Research agents |
| LCM | Concept understanding | Knowledge reasoning | Meta LCM |
What This Means for You
If you're building AI-powered tools, you don't need to understand the math behind each architecture. But knowing which architecture solves which problem will help you pick the right model for your use case:
- Need fast local inference? Use an SLM (Phi-3, Llama 3.2 3B)
- Need to understand images? Use a VLM (GPT-4o, Claude Sonnet)
- Need complex reasoning? Use an LRM (o1, Claude with thinking)
- Need to automate tasks? Use a LAM (Claude Code, browser agents)
- Need everything? Combine multiple architectures in an agent stack
The trend is clear: the future of AI isn't one model doing everything — it's specialized architectures working together.
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