Hurricane Sentinel in Plain English

Summary

Sentinel is a safe place to run AI agents on your own computer. An agent is an AI that doesn't just chat — it takes actions: reading files, running commands, calling out to the network. Sentinel lets it do useful work while keeping it on a tight, enforced leash and keeping a human in charge of anything risky.

Just talk to it

You drive Sentinel from a clean little chat window in your terminal. You type what you want; the agent works on it and shows you, step by step, what it's doing — which AI it's asking, which tools it's using — and types out its answer at the end. When it wants to do something you've marked as risky, it stops and asks you right there: yes or no. Start a line with a slash for commands (like switching agents or picking a different AI model), with an @ to hand a job to a particular specialist, or with a $ to browse the skills you can give an agent — and a little menu pops up to help you. Your past messages are remembered, so the up arrow brings them back.

The three guards, working together

Sentinel is the conductor for the other two Hurricane tools:

• Muzzle sits in front of the AI model and checks everything going in and coming back out.
• Leash gives the agent its own user account on the machine and lets the operating system decide what it can touch, reach, and run.
• Sentinel runs the agent inside both of those guards, and adds the missing piece: a human approval step.

Because the agent runs as a real, leashed user account, the limits aren't a promise the AI makes — they're enforced by the computer itself.

Stop and ask a human

This is the part that makes Sentinel different. You decide which actions are fine to do automatically, which are flat-out forbidden, and which should stop and ask. When the agent reaches for a "stop and ask" action, Sentinel freezes the task right there, saves its place, and waits. Later you look at what it wants to do and say yes or no. If you say yes, it picks up exactly where it left off and carries on. If you say no, it's told no and keeps working around it. Nothing risky happens without a person's say-so.

Never loses its place

Every task is saved as it goes. It can pause for an approval for as long as it needs and resume later from the exact same spot — nothing is lost, and you always have a record of what happened.

A team of specialists

You don't run one all-purpose AI — you build a small team. Each agent has its own personality, its own know-how (skills), and even its own AI model. One of them is the manager: when you give it a job, it picks the right specialist from your team and hands the task off, or — if nobody on the team fits — it creates a temporary helper just for that job. You can keep a good temporary helper around for next time, throw it away, or have Sentinel ask you. Every hand-off still goes through the same "stop and ask" approval, and each helper is held to only the abilities you've given it.

It remembers

Each agent keeps its own notebook. When it learns something worth keeping, it writes itself a note; later — even in a completely separate session — it can look that note back up and use it. The notebooks are private to each agent by default, but you can let a few agents share one. So your specialists actually get better over time instead of starting from scratch every run. And because a long task can fill up an AI's short-term memory, Sentinel quietly tidies up as it goes — summarizing the older back-and-forth while keeping the important recent bits — so nothing important gets lost and the agent never runs out of room.

Made to work with small AIs

You shouldn't need an enormous, expensive model to get reliable results. Sentinel does a lot of quiet work so even small models running on your own machine behave well: it tells the model clearly how to use its tools, understands a tool request even when the model phrases it sloppily, fixes up small mistakes and asks the model to try again, and makes sure a specialist always gets the full original request — so a manager's terse hand-off never drops the details.

Save a recipe and reuse it

For jobs you do over and over, you can save a workflow — a little recipe of steps. Step one might be "have the researcher gather the facts," step two "have the writer turn them into a summary." Sentinel runs the steps in order and automatically hands each step's result to the next, so the writer gets exactly what the researcher found. You give it a starting input, run it by name, and it does the whole sequence — and every step still runs under the same guards and approvals as anything else.

Where it's at

Sentinel is in the lab. The heart of it — running an agent under the guards, pausing for approval and resuming, a manager agent delegating to a team of persistent specialists, agents that remember things across sessions, a clean chat window to drive it all, and saved multi-step workflows — is built and has been tested working end to end on Linux, including with the operating-system enforcement switched on and actively blocking the agent from off-limits files, and producing correct results on local models. The remaining piece — a local web dashboard — is still being built. It runs on Linux only for now, and there's no public download yet.