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To balance AI autonomy and human oversight, apply a framework managing agency-control tradeoffs through continuous calibration loops and structured graduation criteria for AI agents. This ensures autonomous task performance retains necessary human safeguards.

An eval strategy for AI agents is a structured evaluation plan encompassing unit, behavioral, adversarial, and production evals. It tests AI behavior across scenarios to ensure alignment, prevent risky autonomous actions, and build user trust before staged rollout.

Design a calibration plan by implementing continuous calibration and confidence development loops. This involves structuring data collection, recognizing behavioral patterns, and establishing rollout governance to iteratively adjust AI agency levels and improve model consistency.

Yes, this approach specifically applies to AI agents and LLM-enabled features performing user tasks autonomously. It provides risk management, staged rollout, and governance artifacts to safely manage high-agency AI products where direct human review is minimized.

The best way to build trust in AI-powered products is combining confidence-building methods with staged rollout governance. Formalizing trust-building artifacts alongside structured agency graduation criteria ensures users understand AI capabilities and limitations during deployment.

You need governance artifacts for AI-native product delivery when shipping features with autonomous AI actions. They formalize risk management, outline eval strategies, and establish calibration plans to prevent misalignment and inconsistent behavior in production environments.