How to Launch an AI Agent for Free: Permissions, Demos and Trust
Launching an AI agent for free comes down to trust in what it does, not in what it says. Publish a short list of what the agent can read, change and send, make every risky action wait for approval at first, show one recorded run from start to finish, and post it where builders of agents already look. Keep the first users few, and read their logs yourself.
Why is an agent harder to launch than an AI app?
A text generator can only be wrong on the screen. An agent can be wrong in someone’s inbox, calendar, repository or bank export, and a visitor knows it. Before a stranger gives your agent access to anything, they ask what it can touch, what it can send, and how they undo a mistake. If your page does not answer those three questions, nothing else on it gets read.
So the unit of trust is the permission, not the output. Plan the whole launch around making permissions small, visible and reversible. The AI app playbook covers the model-bill side, which an agent shares; this page is about the part that is not shared.
Which permissions should an agent start with?
| Action | Launch default | Loosen it when |
|---|---|---|
| Read data the user pointed it at | On, scoped to that folder, label or project | Never widen beyond what the task needs |
| Write or edit that data | Propose a diff, apply on approval | A user has approved the same kind of change many times |
| Send messages or emails | Draft only, user sends | Rarely; keep a daily send limit even then |
| Spend money or call paid APIs | Off, or a small fixed budget | The user sets the budget themselves |
| Delete anything | Off | Only with a restorable trash |
Put this table on the page in your own words. Users read a refusal to act as seriousness, and the approval step doubles as the place where you learn which actions people actually trust.
How do you demo an agent without a live failure?
Do not run the agent live on launch day. Record one complete run on a task a stranger would recognise, with the screen showing each step the agent took, and publish the recording beside a text log of the same run. People trust a log they can scan faster than a video they have to sit through.
- Pick a task with a clear before and after, such as turning a folder of receipts into a filled expense sheet.
- Run it ten times on practice data and keep the run that is typical, not the best. Show one retry if there was one.
- Record the plan the agent made, the tool calls, the approvals you gave and the final result.
- Add a sentence on what it could not do in that run. Honest limits make the rest believable.
- Offer a sandbox with fake data, so a visitor can watch it act without handing over anything real.
The demo video script template gives a structure for the recording, and the answer on recording a demo covers the screen-capture basics.
Where do people who want agents look for them?
- Code hosting pages. If your agent is open source, the repository README is the product page. Put the permission table and the recorded run at the top.
- Show HN and similar technical boards. They reward something others can try; a sandbox suits them better than a sign-up wall.
- Tool registries. If your agent exposes tools to other software, see the MCP server playbook for the registry route.
- Free launch boards. The free launch form here gives you a public page with no queue, which is useful while you wait on the slower channels.
The agent-specific places and the Product Hunt alternatives for AI agents list where else it is worth asking. Most of those pages have no promise of attention, so spend time on the two or three where the audience builds or buys agents.
What does a three-week launch look like?
- Week one: write the permission table, add an action log the user can read, and wire in the approval step for every write.
- Week two: give it to five people who have the problem. Sit in on their first session, read their logs and fix the actions they declined or undid.
- Week three: record the run, write the page, publish on one board and one community, and stay available for questions for two days.
How do you measure whether people trust it?
Count the share of proposed actions that users approve unchanged, the share they edit, and the share they reject. Approved-unchanged rising over the first weeks is the signal that permissions can widen. Many edits point to a planning problem; many rejections point to an action the agent should not be proposing at all. Also note how many users stop after the first run, which tells you whether the setup was too heavy.
Keep a short test list of tasks the agent must complete and tasks it must refuse, and run it before every release. For ideas on what agents people want built, browse the AI agent ideas on the board.
Frequently asked questions
Should an AI agent ask for approval before every action?
At launch, yes for anything that writes, sends, spends or deletes. Reading can run freely inside a scope the user chose. Approvals give you data on which actions users trust, and you can relax them for a given action once people have approved it unchanged many times. Never remove approval for deletion or spending.
Can I launch an agent with no sign-up?
You can launch a sandbox that way: fake data, no real accounts connected, a short session. Anything that connects to a user’s real accounts needs authentication, so make the sandbox the thing people try first and the sign-up the step after they have seen it work.
Where do I show an agent’s logs publicly?
Publish one recorded run as text on your landing page or README, with personal data removed. It lets visitors judge the plan, the tool calls and the result in a minute. Keep live users’ logs private, and tell users what you store and for how long.
How many testers does an agent need before launch?
Five to ten people who really have the problem is enough to find the permission and planning faults, as long as you read their runs yourself. More testers without that reading teaches you little. Widen the group only when most proposed actions are approved unchanged.
Written by Post your build ideas with AI assistance and edited for this site. Where a page names another product, the facts came from that product’s own pages on the date shown above; terms change, so check them before you rely on them. About the publisher.