Topic

Agent Drift

Agent drift refers to the gradual or sudden degradation in an AI agent's performance, behavior, or alignment with its intended objectives over time, often caused by data distribution shifts, model decay, or uncontrolled feedback loops. This phenomenon is critical for enterprise AI governance because unchecked agent drift can lead to unreliable outputs, compliance violations, and eroded trust in autonomous systems before governance teams detect the problem. Organizations must implement monitoring systems, performance baselines, and retraining protocols to detect and correct agent drift before it causes business or regulatory harm.

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Corporate PolicyUS2026-07-02

Attentive's Five-Step Agentic AI Governance Framework Offers a Replicable Enterprise Blueprint

Attentive published a practitioner implementation guide outlining five steps for governing agentic AI systems, including creating an agent registry, assigning scoped identities and least-privilege permissions, and defining behavioral guardrails. The guide targets enterprise teams deploying AI agents and recommends starting with the highest-risk agents before scaling governance patterns across the organization. It emphasizes human-on-the-loop oversight and continuous monitoring as core controls for mitigating agent drift and unauthorized tool use.

agentic AI agent registry least-privilege human oversight Attentive non-human identity agent drift enterprise governance