Agent Governance Framework

Google Cloud's four-vector framing for evaluating whether enterprise agents are behaving acceptably in production.

Key points

• Policy adherence: agents obey internal rules such as "do not write to external-facing databases" and the organization can prove that adherence ^[src-043].
• Intent loyalty: agents remain faithful to the original user or business intent across tool calls and agent handoffs ^[src-043].
• Safety guardrails: agents behave ethically and in line with the company's brand, safety posture, and responsible-AI rules ^[src-043].
• Fiscal responsibility: agents use tokens and tool/API/MCP calls judiciously and reserve limited budget for the right priority work ^[src-043].
• The framework is explicitly tied to observability: enforcing rules is insufficient unless teams can observe adherence, drift, violations, and attempted violations ^[src-043].
• QuantumBlack / McKinsey adds the sprawl and autonomy angle: enterprise agent governance has to make agents discoverable, permissioned, evaluated, observable, and auditable before organizations can safely scale beyond pilots ^[src-111].
• The report also links governance to human adoption: trust, escalation, behavior monitoring, and clear autonomy boundaries are operating-model requirements, not afterthoughts ^[src-111].

Related concepts

• Enterprise Agent Governance
• Intent Loyalty
• Agent Budget Controls
• Tool Schema Tax
• Governance Observability
• Agentic AI Mesh
• Agentic AI Operating Model

Source references

• ^[src-043] Google Cloud Events — "Operationalize AI: A blueprint for managing enterprise agents at scale" (2026-04-24)
• ^[src-111] QuantumBlack / McKinsey – "Seizing the agentic AI advantage" (2025-06)