LLM (large language model)
A large language model is a neural network trained on enormous amounts of text to predict the next token given previous tokens. The 2024-2026 frontier LLMs (Claude, GPT-4, Gemini) underpin most modern AI agent products, including Ajeris.
Why it matters
The LLM is the brain of a modern AI agent. Without one, the agent has no reasoning capability; it is just a tool router. With one, the agent can read intent, plan, decompose tasks, recover from tool failures, and produce coherent natural-language responses.
The capability ceiling of an agent is bounded by the LLM underneath, but the user experience is shaped by everything around it: the channels the agent is reachable on, the memory it has, the tools it can call, the operational guardrails. The LLM is necessary but not sufficient.
The 2026 frontier model landscape is dominated by Anthropic's Claude family, OpenAI's GPT-4 family, and Google's Gemini. Each has strengths and weaknesses; most modern agent products either pick one as a default or route between them based on the task. The capability differences between frontier models on common operational tasks are smaller than the architectural differences between agents that use them.
How Ajeris handles this
Ajeris uses Anthropic's Claude as the default reasoning model. The choice is deliberate: Claude's tool-use API and instruction-following behavior are well-suited to the agent loop Ajeris runs. We route to other model providers when the task calls for it (image generation, certain coding tasks).
The model is invisible to most users. The agent answers in natural language; the user does not pick a model or know which one is running. For users who care, the model tier is configurable.
The capability ceiling rises every few weeks as Anthropic ships new Claude versions; we absorb the upgrades without user-visible changes.
