Why We Exist

The Hallucination Problem

AI agents are going to production at an unprecedented rate. Enterprises are deploying conversational agents, autonomous workflows, and AI-powered decision systems across every industry - from healthcare to finance to customer support.

But there’s a fundamental problem: these agents hallucinate. They fabricate facts, misquote policies, invent data, and confidently present fiction as truth. And unlike a bug in traditional software, hallucinations are probabilistic - they don’t reproduce reliably, they’re hard to detect, and they erode trust silently.

Why Now

The shift from AI as a tool (autocomplete, summarization) to AI as an agent (autonomous decision-making, multi-step workflows) has changed the stakes dramatically:

• Agents act on their outputs. A hallucinated API call, a fabricated customer record, or a misinterpreted policy doesn’t just produce wrong text - it triggers real-world actions.
• Scale amplifies harm. One hallucinating chatbot serving 10,000 customers per day can cause more damage in an hour than a human agent would in a year.
• Detection is hard. Traditional testing (unit tests, integration tests) doesn’t catch probabilistic failures. You can’t write a test for “don’t make things up.”

What We Believe

We believe AI agents should be held to the same engineering rigor as the rest of the software stack. Just as you wouldn’t deploy a database without backup and monitoring, you shouldn’t deploy an AI agent without evaluation, guardrails, and observability.

Future AGI exists to make that possible - to give engineering teams the tools to simulate, evaluate, protect, and optimize their AI agents before and after they reach production.

The Gap We Fill

Before Future AGI, teams had to choose between:

• Vibes-based evaluation - manually testing prompts and hoping for the best
• Building internal tooling - spending months building evaluation pipelines that are never comprehensive enough
• Observability-only solutions - knowing something went wrong after it happened, but not preventing it

We provide the full lifecycle: test before you ship, protect in production, and improve continuously with data.