Can AI Be Trusted With Automating Complex Accounting? Vibe Accounting in Public

Can AI actually be trusted with automating complex accounting? Instead of just debating it, we're going to find out. Welcome to Day 1 of our project to build and benchmark a reliable AI agent, starting with one of the toughest challenges: revenue recognition. And we won't stop until the entire monthly revenue close is automated.

Our philosophy is simple: AI doesn't get trust; it earns it.

That’s why we’re not just pointing a raw LLM at a problem. We’re building a secure, controlled environment (the "Architectural Guardrails" I've discussed before) to channel its power responsibly. Here’s a deeper look at the controlled environment we've engineered:

1. A Safe and Specialized Language

We don't let the AI operate with unlimited possibilities. We've built it a custom, JavaScript-based Domain Specific Language (DSL). We chose JS as a base because most LLMs have been pre-trained on vast amounts of it, giving us a head start on its understanding. But our version is highly restricted. We've removed any function that could interact with outside systems, access the file system, or perform destructive actions. The AI can only "speak" and "think" in terms of secure, specific accounting, and data manipulation tasks.

2. A "Flight Simulator" with an Instructor

The agent's entire workspace is a completely isolated sandbox. Here, it can write code using its specialized language and test it against our validation engine. This isn't just a pass/fail check - the validator provides structured error feedback that the AI is designed to understand, allowing it to iterate and self-correct its own mistakes. But like any training simulation, nothing leaves the room without expert approval. A human "pilot" must review and sign off on all code before it's ever considered for production.

3. A "Perfect Memory" of Principles and Particulars

An AI's output is only as good as its context. We provide it with two layers of memory. First, the "principles": it gets full context of Leapfin’s core architecture, like our Universal Accounting Records and Connected Accounting Map. This teaches it the universal grammar of sound, traceable accounting. Second, the "particulars": we auto-feed it the specific company’s chart of accounts, custom policies, and data sources. This gives the AI the specific dialect it needs to work accurately and autonomously within that company's unique environment.

The environment is built. The controls are in place. Now, the real work begins!

Stay tuned as we put our first agent to the test and transparently benchmark its performance on accuracy, cost, and speed.