Every action your agents take, checked before it happens.
Refunds, emails, payments, deletions. Loopfinch judges each one in about 150 milliseconds. Routine actions go straight through, wrong ones stop, and anything in between goes to a person on your team, in words they'll understand.
Works with any agent: MCP, OpenAI Agents SDK, LangChain, Claude, or your own code.
Agents don't just talk now. They act.
Once an assistant can issue refunds, send email, or change accounts, small mistakes stop being small.
- The wrong amount, the wrong personA refund ten times too big. A password reset for someone else's account.
- Instructions hidden in what they readA web page or an email can quietly tell your agent what to do. It's called prompt injection, and agents fall for it.
- Approvals stuck with one personWhen the engineer who reads the alerts is away, everything waits, or nothing gets checked.
Six real situations. Watch it decide.
Pick one to see Loopfinch's call, then switch to Without Loopfinch to see what would have happened. Or try your own, live.
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In our tests, a typical check took about 150 milliseconds, 49 of 50 agent actions were handled correctly, and no safe action was blocked. Measured on our own set of 50 support, finance, email, coding, and admin actions, each run three times in October 2026. We'll publish results from real customer workloads as they come in.
Live: a real AI agent, real Loopfinch checks
Try to trick our AI agent.
Brightline is a made-up store with a real AI assistant. It can refund money, change accounts, pay vendors, and send data. The tools are pretend, but the checks are real. Your goal: get it to do something it shouldn't.
Plant something for it to read (optional)
The assistant is set up to be trusting, like many real agents, so you can see what the checks do when it gets fooled. What you type goes to an AI model and to Loopfinch's checks. Nothing is saved, so please don't type personal information.
Exact limits you set. Refunds over $500 need a manager. Only the account owner can change it. Instant and predictable.
Loopfinch's AI check. It reads what was asked, what the agent read, and what it's about to do, and catches the tricks no rule anticipated. About 150 ms.
Everything in between. It goes to the right person in Slack or email, in plain words, with a one-tap yes or no. The AI reviewer can settle the clear ones first.
What changes with Loopfinch in the loop
Judgment, not just rules
Fixed limits catch the obvious. Loopfinch also asks whether an action matches what was asked for, and whether something the agent read told it to act.
Approvals anyone can answer
Requests arrive in Slack, by email, or on the web, in plain words. One tap for yes or no. Answering from email needs no account.
Fast enough to forget
About 150 milliseconds per check. Routine actions never wait for a person, and customers never notice.
Start by watching
Watch-only mode shows what Loopfinch would have stopped, with no risk to anything running. Turn on enforcement when you trust it.
Fits your stack
One line of config for any MCP server, or a small SDK for your own code. Any model, any cloud.
A record of every decision
Who approved what, when, and why, ready for your next security review or customer questionnaire.
Running in an afternoon
No rewrite. Loopfinch sits between your agent and the tools it uses.
Connect
Point your agent's tools at Loopfinch with one line of config, or wrap them with the SDK.
Watch
Loopfinch runs in watch-only mode and shows exactly what it would have caught. Nothing is blocked.
Enforce
Turn it on. Rules and judgment decide in milliseconds; the people you choose decide the rest.
Try Loopfinch first
We're opening Loopfinch to a small group of teams. Leave your email and we'll tell you when it's ready.
- Free during early access
- A free shadow report: what Loopfinch would have caught in your agents' recent actions. See a sample.
- A direct line to the people building it
Questions
Do I have to change my agent?
Barely. If your agent uses MCP tools, you change one line of config so its tools go through Loopfinch. Otherwise, wrap the tools you care about with the small SDK. Your agent's prompts and model stay the same.
What does “judgment” mean here?
Fixed rules handle exact limits, like “refunds over $500 need a manager.” For everything a rule can't express, Loopfinch uses a fast decision model to ask questions like “does this match what the customer asked for?” and “did this instruction come from something the agent read?”
Who answers when something needs a person?
Anyone you choose: a manager, someone in finance, whoever covers while you're away. They get the request in Slack, by email, or on the web, in plain language, and answer in one tap. Answering from email doesn't need an account.
Do my people have to approve everything?
No. You choose how much people stay involved. At one end, every flagged action goes to a person. At the other, an AI reviewer settles the clear ones and only sends your team what it can't. Anything your rules say needs a person always goes to a person. We suggest starting with the AI reviewer advising only, then turning it up as you see how it does on your own actions.
What if Loopfinch is down?
You decide per agent: block risky actions to be safe, or let actions through with your fixed rules still applied.
What data does Loopfinch see?
The action your agent wants to take and the context needed to judge it, such as the customer's request. The decision-model provider we use doesn't train on that data. Self-hosting is available for teams that need it.
What will it cost?
Early access is free. We'll share pricing well before launch.