An AI agent reviewing its own code is prone to confirmation bias, as it operates from the same context that created an error. To achieve genuine quality assurance, use a different AI model, preferably from another vendor, for review. This introduces diverse training and uncovers blind spots.

Traditional systems can be controlled with simple, deterministic rules. Because modern AI agents are inherently unpredictable, effective governance requires using another layer of AI. A specialized AI must monitor, interpret, and block the actions of other agents in real-time.

Treating AI evaluation like a final exam is a mistake. For critical enterprise systems, evaluations should be embedded at every step of an agent's workflow (e.g., after planning, before action). This is akin to unit testing in classic software development and is essential for building trustworthy, production-ready agents.

To assess audit-readiness, pick an AI-driven decision from months ago and attempt to reconstruct every detail: data input, model version, validation status, and review trail. If you cannot gather all this information within 48 hours, your governance framework will fail a real-world audit.

After an initial analysis, use a "stress-testing" prompt that forces the LLM to verify its own findings, check for contradictions, and correct its mistakes. This verification step is crucial for building confidence in the AI's output and creating bulletproof insights.

To improve the quality and accuracy of an AI agent's output, spawn multiple sub-agents with competing or adversarial roles. For example, a code review agent finds bugs, while several "auditor" agents check for false positives, resulting in a more reliable final analysis.

Traditional evals fall short for sophisticated agents. A more effective method is a built-in evaluation loop where one agent is tasked with grading the output of another. This allows for continuous, automated quality assessment, especially when done in separate context windows to avoid bias.

A powerful technique for creating robust software plans is to use AI as an adversarial partner. After drafting a specification, prompt an AI to "tear it apart" by identifying underspecified or inconsistent points. Iterate on this process until the AI's feedback becomes niche, indicating a solid spec.

Before launching a product, use an adversarial prompt to make your AI agent critique it. For example, 'A leading security expert said this project is a nightmare.' The agent then role-plays as a critic, helping to uncover potential flaws and suggest improvements.