Ascent Performance

Unfenced is an engine that builds AI agents, trains them against realistic customers, and keeps them learning after they go live. You describe the agent you want in plain words. Unfenced writes its card (what it does, what a good and bad result look like, what it must never do), builds it, and lets you add your own data. Before it meets a real person, it practises in the Arena against agents made from real customer behaviour, not polite AI talk. Up to 10 matches play at once, and you watch every chat live, won or lost. If you don't like a reply, you correct it on the spot. The correction takes a short exam before the next match uses it. An agent goes live only after it passes a gate set by risk: 7 of 10 wins for most jobs, and 16 of 20 with zero safety failures for high-risk fields like money or customer data. After that, every real case flows back into the Arena. Each night the agent studies its losses and writes its own lessons, and every lesson passes the same exam before it is used. Agents in the same field learn from each other's mistakes, so each new agent makes the whole engine smarter. The first agent is Sales for a football boot store on WhatsApp, replying in Egyptian Arabic, English and Franco. Any agent you build runs on the same engine. Built on Meta's Muse models.