AI Coding Agents Are 77% Accurate. That's Not the Problem.

DualEntry tested popular AI models on accounting workflows and found they top out at 77.3% accuracy. The accounting press treated this as a warning. It's actually a useful data point about how to deploy agents correctly.

We run AI coding agents for clients at HypeLab. None of them operate autonomously at 100% trust. Every agent has a review loop — a human checks the output before it ships. The 77% number isn't scary if you've built your system around the assumption that agents make mistakes.

The mistake companies make is binary thinking: either AI does the job or it doesn't. The correct frame is that an agent doing 77% of the work correctly still saves your team 77% of the time on that task. The remaining 23% is a review problem, not a failure.

What matters is the review loop. Can a human quickly verify the agent's output? Can the system flag low-confidence work for closer inspection? Can you measure accuracy over time and improve the prompts?

We track accuracy per task type across every client deployment. Some tasks hit 95%+ after a few weeks of prompt tuning. Others sit at 60% and stay there — those get restructured or pulled back to manual.

The companies getting burned by AI agents aren't the ones with accuracy problems. They're the ones who skipped the review loop entirely.