Panel Review by TruVerifAI

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Panel Review is the guardian agent for AI's highest-stakes coding decisions. Before an agent's riskiest designs, diffs, or commits ship, four frontier models, from OpenAI, Anthropic, Google, and xAI, argue them through and return severity-tagged findings, with review gates the agent cannot silently skip. A single model reviewer can share blind spots with the model that wrote the code, including a documented tendency to rate its own family's output more favorably. Four independently trained models don't share those specific gaps: on hard review cases, different models catch different failure classes, and no single model produces the complete findings set. Panel Review's consensus mechanism finds where the models genuinely disagree and makes them argue it through: cross-examination, not polling, so the strongest argument survives instead of the disagreement getting averaged away. That is what makes it useful inside the agent loop: it catches blind spots before the write and before the commit, not after a PR is open. In ten weeks of live production use building this product, 46% of audits surfaced at least one critical or major issue and 66% of acted-on review calls changed the agent's decision, including a prompt injection hole caught one commit from production. The client is open source with zero runtime dependencies, so you can npm pack and read every line before installing: no build step and no transitive supply chain, MIT licensed.