6 AI Agent Models Explained 🤖

Choosing the right LM is crucial for building AI agents

But how to find which one to use? Let me break it down...

Language models are the brain powering next-generation AI agents, making it important to understand the popular types available.

📌 Today, let me break down the 6 popular LMs used in AI Agents:

1. GPT (General Pretrained Transformer)
- Example: GPT-3.5, Claude 3.5
- Predicts next words to generate fluent text, answer questions, chat, and complete tasks from prompts.
- Workflow:
a. Pretrain on massive text → Process through transformer layers.
b. Calculate next-token probabilities → Sample and decode output.

2. MoE (Mixture of Experts)
- Example: Deepseek V3, Mixtral 8x7B
- Activates select expert networks per input for efficient understanding and generation.
- Workflow:
a. Train multiple expert networks with a gating system.
b. Route input to top-k experts → Merge outputs for final result.

3. LRM (Large Reasoning Model)
- Example: Deepseek R1, OpenAI o1
- Performs multi-step reasoning and planning for more consistent, explainable AI responses.
- Workflow:
a. Generate chain-of-thought reasoning steps.
b. Evaluate reasoning paths → Output logic-supported answer.

4. VLM (Vision Language Model)
- Example: Qwen2.5-VL, GPT-4o (Vision)
- Understands images and text jointly to answer questions, describe, or generate multimodal content.
- Workflow:
a. Encode images and text into unified embeddings.
b. Cross-modal attention → Joint processing for multimodal output.

5. SLM (Small Language Model)
- Example: Google Gemma, Microsoft Phi
- Compact model for efficient, on-device language tasks like generation, summarization, or classification.
- Workflow:
a. Compact architecture with fewer transformer layers
b. Efficient on-device processing → Quick token generation

6. LAM (Large Action Model)
- Example: Salesforce xLAM, Rabbit AI’s R1
- Plans and executes structured actions or API calls from prompts for autonomous task completion.
- Workflow:
a. Plan action sequences from task descriptions.
b. Execute API calls in the environment → Monitor and iterate.

Now, which LM should you choose?

📌 Let me give you an overview:

1. GPT - Generate answers and call tools as per input
2. MoE - Handle high-volume requests with efficient resource usage
3. LRM - Complex problem-solving with multi-step reasoning
4. VLM - Computer-using AI agents and visual task automation
5. SLM - On-device agents with limited computational resources
6. LAM - Autonomous task execution and workflow automation

This is just an overview,
For a detailed guide on building agents - including LM selection, frameworks, and enterprise mindset - check out our new course.

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