Show HN: Four frontier models argue over your AI agent's riskiest code

Given that each model has its own blind spots and jagged intelligence, I used to run one model's output by another to get the most reliable outcome. Then it occurred to me that humans always arrive at truth through argument and consensus (e.g., courts, sciences etc.) I applied the same principle to built https://truverif.ai/home that utilizes multiple models (arguing with each other) to arrive at the final outcome.

While looking for practical applications, I realized coding is the space that needs this most because it has the highest AI agent adoption and the mistakes made by agents cost 30x more if they are discovered in production (added to the loss of customer trust). So, we developed Panel Review - it routes an AI coding agent's riskiest diffs, designs, and commits through a panel of four frontier models (e.g., OpenAI, Anthropic, Google, and xAI). Tool has review gates the agent cannot skip. We built these gates after learning the hard way that agents don't follow written instruction to do a review. In ten weeks of live production use, the panel found at least one critical or major issue in 46% of the reviews and 70% of the reviews resulted in the agent changing implementation based on panel recommendations. For example, one review caught a prompt injection hole in our own MCP server. Happy to answer any questions.