Creating a Basic Revenue Recognition Automation Rule | Vibe Accounting in Public - Day 2

It turns out the problem wasn't the AI.

On day 2 of our Vibe Accounting in Public project, we put our new AI agent to its first test: creating a basic revenue recognition automation rule. I recorded the raw, unedited first run, and as you'll see in the video, its first output was incomplete.

But the reason why is the most important lesson for anyone working with AI. It wasn't an "AI failure." The agent correctly and logically processed the ambiguous prompt I gave it.

This highlights a new dynamic: AI doesn't always guess or fill in gaps like a human might. My initial prompt was intentionally general, and the AI returned a logical, general result. But when I provided one sentence of specific context, it delivered the perfect automation instantly.

This proves the bottleneck for AI accuracy isn't the model's intelligence - it's the clarity of the human expert directing it. The future of accounting isn't about finding an AI that can read our minds. It's about training accountants to become precise, expert directors of AI agents.

(A quick note on the setup: We're interfacing directly with the agent's code output for now. Our focus for now is on nailing the core logic, not the UI.)

So yes, this was a massive success. It proved our agent is incredibly responsive to expert direction. The real question is: are we ready to be that precise?

Next, we'll raise the stakes with much more complex revenue recognition.

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