Ollama Sidecar explores a practical question: can an AI agent running in a hardened Docker environment call on a small local language model without sending every task to a hosted model? The answer is yes, at least for modest work. The agent runs in its own sandbox, asks a local Ollama model to generate a simple HTML page, and keeps the model service in a separate container on the same private Docker network.
The interesting part is not the web page. Nobody needs a parade because a model wrote some HTML. The useful idea is that an agent can be given access to small or specialized models as nearby helpers. A business could use that pattern for contained writing tasks, formatting work, classification, drafting internal snippets, or other narrow jobs where a local model is good enough.
This proof of concept points toward a sensible split. Larger hosted models may still be needed for the agent’s harder reasoning and planning. But Ollama gives the agent another option: use a small local model for small local work, inside the same controlled environment where the rest of the agent runs.