Adding an agent means writing a system prompt and pointing it at a model alias. Three ways to set it up: do it yourself, follow the guided setup, or run the Docker image.
https://t.co/uxE0uq3MOY
It runs on your hardware via Ollama + LiteLLM. In my case an M4 Mac Mini. Your phone reaches it over Tailscale, so the chat UI talks to your own machine and nothing leaves your network.
24+ agents, not one chatbot wearing hats: dev advocacy, content and GTM, finance, sales, client delivery, productivity, legal. Each one is just a system prompt plus a model alias.
The shape came from Anthropic's Claude Dispatch, which gave me the mental model. I wanted a local version on my own hardware. So: you type something short, a layer rewrites it into a detailed prompt and routes it to the right agent.
hermes-dispatch: 24+ specialized agents running locally on your own hardware, dispatched from a chat app on your phone. Local models, private routing, MIT licensed. A thread on how it works.
https://t.co/uxE0uq3MOY
hermes-dispatch: Claude Dispatch gave me the mental model, then I wanted a local version on my own hardware. Type a request on your phone, it routes to one of 24+ specialized agents running locally. Nothing hits the internet. MIT.
https://t.co/uxE0uq3MOY
Diagnostic: ask ChatGPT "best [your service] in [your city]."
Not in the answer? It's not an SEO problem. It's a different set of structural signals, and most sites are missing all of them.
https://t.co/rCaAPAoPcL
Post 3 of 6. Post 4 shows exactly where the unspecified territory surfaces and why it consistently stalls at the same point.
https://t.co/xKLlFsizyA
#AgenticAI#DeveloperAdvocacy#AIEngineering
I thought the AI was failing. It was doing exactly what I'd set it up to do.
Every output gap traced back to a decision I hadn't made. What counts as a failed fetch versus an incomplete one? Which fields are required? Who owns retry behavior? I hadn't decided, so the agent did.
It used the most statistically probable answer. Def not the same as the correct one for my specific system.
Writing the spec before opening an AI tool is a design act. The agent can only operate within the specified territory. Everything outside it gets filled with probability.
You're still designing for yourself.
AI coding tools don't fail on prompts. They fail on ambiguous artifacts. When the spec is vague, the agent fills the gaps with what it was trained to assume, not what your system actually needs.
That's the AX Shift. Agent Experience is the real design surface now. The spec, the schema, the interface contract. These are the artifacts your agent executes against. A crisp prompt can't compensate for a muddled spec.
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