The Model Inside the Agent

This part explains what the model contributes to an agentic system and where its limits begin.

The goal is to understand LLMs as probabilistic decision components inside a larger runtime, not as the whole agent.

Part 2: The Model Inside the Agent

• What an LLM Contributes
  Understand tokenization, embeddings, contextual representations, transformer intuition, context windows, next-token prediction, and LLMs as probabilistic decision components.
• Sampling and Behavior
  Learn how logits, softmax, temperature, top-p sampling, reproducibility, and best-of-N strategies shape agent behavior.
• Instructions and Structured Decisions
  Turn prompts into structured, validated decisions for classification, routing, tool use, and untrusted data handling.

Status

This part is planned. Articles will be linked here as they go live.