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].
Related concepts
- Enterprise Agent Governance
- Intent Loyalty
- Agent Budget Controls
- Tool Schema Tax
- Governance Observability
Source references
- [src-043] Google Cloud Events — “Operationalize AI: A blueprint for managing enterprise agents at scale” (2026-04-24)
Recommended next
Keep reading from this thread
From 491 indexed pages and articles.
- Wiki concept Gemini Enterprise Agent Platform Google's enterprise platform for deploying, governing, observing, and securing agents. Related by agent
- Wiki concept LLM Observability The production telemetry layer for AI applications and agents, covering traces, costs, latency, model behavior, tool calls, retries, errors, and cross-service Readers have engaged with this next
- Insight AI Beyond POCs How enterprise AI moves beyond proofs of concept through ownership, governance, measurement, adoption, and production operating models Related by production