wusanto-blindspot

Adversarial review of AI-generated artifacts is performed using persona-based analysis to identify potential failure points and logical gaps in mission wishes, specifications, and handoff documents. This ensures verification criteria are robust and falsifiable.

AI-generated code fails verification because in-loop self-verification relies solely on the executing agent's own judgment, which can miss real-world errors caused by literal interpretation or vague mission requirements. Adversarial review mitigates this risk.

The best way to ensure acceptance criteria are falsifiable is to apply falsifier-driven findings during an adversarial review. This guarantees every concern raised is testable and actionable, preventing vague feedback from being accepted.

Yes, you can use persona-based analysis to validate handoff documents before accepting complex coding tasks. It checks if the agent's plan relies on in-loop self-verification that might fail to catch real-world errors.

You need to run an adversarial review on a PlanningPack before proceeding with a complex coding task from an AI agent. This identifies potential risks and verifies that the mission requirements are robust and not vaguely defined.

Relying solely on an executing agent's judgment for quality assurance risks accepting AI-generated work without proper verification. This leads to errors from literal interpretation or vague requirements, which adversarial review with external personas is designed to catch.