Decision Boundaries

Decision boundaries in AI systems define the thresholds at which models transition from one classification or action to another, such as when a loan application moves from approved to denied or when content triggers moderation flags. Establishing clear, documented decision boundaries is critical for governance because they directly impact fairness, accountability, and regulatory compliance across high-stakes applications. Organizations must monitor whether these boundaries remain stable over time and whether they produce disparate impacts on different demographic groups, making boundary management a key component of responsible AI deployment.

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Corporate PolicyGlobal2026-07-25

IBM's Agentic AI Governance Playbook Sets an Industry Benchmark for Autonomy Boundaries and Approval Controls

IBM has published an Agentic AI Governance Playbook advising organizations to define agent purpose, scope, and decision boundaries before development begins. The playbook recommends limiting access to workflows, APIs, and enterprise systems, and prescribes approval workflows, risk classification, and adversarial testing as pre-deployment requirements. The guidance applies globally and is directed at enterprises across industries deploying or planning to deploy AI agents.

IBM agentic AI approval workflows decision boundaries access controls adversarial testing agent governance least privilege