# Your local LLM app needs guardrails before it needs prompts > Source: > Published: 2026-08-27 14:03:17+00:00 Most local-LLM tutorials start with the fun part: the prompt. After running a fleet of autonomous agents on local models 24/7 and logging every failure — the ledger now holds over eight thousand entries — we start somewhere else. Here is the single most important thing that ledger taught us: **the call succeeding tells you nothing.** The majority of our contract violations were outputs that were So we distilled the survival kit into a scaffold you can stand up in one command: ``` npx create-local-llm-app my-app ``` Five files, about 180 lines, plain Node and Express, no framework lock-in. What gets wired in: A contract declares what "done" looks like — and then verifies the artifact itself. Never a proxy. Not the exit code, not a log line, not HTTP 200. `contract.js` has three kinds of clause: `minChars` — the cheapest check, and statistically the most valuable`must` — patterns that have to appear`mustNot` — leftover placeholder tags, unfinished markers, refusal leakage ("as an AI, I cannot…" inside what was supposed to be a business document)Every `mustNot` entry exists because it caught a real production failure. When the contract rejects an output, the failure reason goes into the next prompt. Three strikes and the item is rejected outright — there is no "accept with warnings", because accept-with-warnings is "apologize later" with extra steps. Nothing the model writes leaves the app without a person pressing approve. No decision means no. That rule comes from experience: an unattended script once contacted a real company because its *default* was "send". The dangerous side is never the default here. A heartbeat (is the process alive?) and a silent-zero check (is anything actually being produced?). These are different questions. Running is not the same as producing — a fleet can be 100% "up" with zero output all day, and nothing in a standard health check will tell you. Because the guardrails are the product; the model is replaceable. Swap Ollama models in `.env` , replace the prompts with your own business task, and the contract/queue/watchdog skeleton stays exactly where it is. If you want to go deeper, the checker we run in production is free and MIT: [honto-contract](https://www.npmjs.com/package/honto-contract). A free snapshot of the failure ledger behind all of this is on [Hugging Face](https://huggingface.co/datasets/GXCafe/ai-agent-failure-logs). Start with the scaffold, break every guard on purpose, and watch them hold. That is the fastest way to trust your own unattended setup.