Something real is shifting in how developers work. Agents open up work that used to be off-limits, not because it was technically impossible, but because it required niche expertise most of us didn't have. Containerization, inference server configs, model-specific environment setup: these are the kinds of tasks that used to demand either deep expertise or hours of self-education before you could even get started. Agents allow for an elegant way to bridge those pre-requisite knowledge gaps. You describe what you want, and the agent fills in the knowledge gaps.That's the unlock. Not speed. Access.‍The day Netflix dropped a new modelNetflix recently released void-model on Hugging Face. The day it came out, my instinct was the same as always: I want to try this. But wanting to try a new model and actually running it are two different things. Getting it into a usable environment, handling the inference server setup, figuring out the container configuration, wiring it all up correctly: that's the part that usually introduces a day or two of lag between "this looks cool" and "okay I'm actually using it."This time, that lag was basically zero.Using Goose, a CLI agent runner, combined with Together's dedicated containers skill, I went from "Netflix just dropped a model" to "I have a running container for it" in a single session. The agent produced all the code needed to deploy void-model on Together's Dedicated Container Inference (DCI) infrastructure, essentially on release day.The output lives here: github.com/blainekasten/together-void-model-container‍Exactly what I didThe whole setup took three steps.Step 1: Install the Together dedicated containers skill.npx skills add togethercomputer/skillsThat pulls in the together-dedicated-containers skill, which gives Goose the specific knowledge it needs to work with Together's infrastructure: how to configure the inference server, what the container spec should look like, how to wire everything up for a given model.Step 2: Start a Goose session and run one prompt.I want to deploy this model on togethers dedicated containers https://huggingface.co/netflix/void-modelThat's it. One sentence.Step 3: Sit back and watch it work.From there, the agent pulled the model details from Hugging Face, figured out the right inference server configuration for the model architecture, generated the container config files, and produced a complete, runnable setup, all without me having to look anything up or guide it through individual steps.The result: blainekasten/together-void-model-container, a clean, working repo anyone can use to run void-model on Together infrastructure.Step 4: Use your model!After the agent deploys your application you can start running inference against it. The Together CLI has commands to easily test inference.