03/09/2026
Table of ContentsThe pilot phase is over. Can your business handle agentic AI in production workflows? Organizations increasingly deploy agentic AI systems – that can plan, decide, and act to support collaboration, automation, and analysis – into production workflows across multicloud environments.
Google Cloud’s ROI of AI 2025 study reflects this shift, reporting that 52% of executives say their organizations have deployed AI agents in production, while 39% report running more than 10 agents today, signaling that multi‑agent environments are already common.
This pattern is already evident in Google Cloud environments, with the use of Vertex AI to build and deploy production‑grade AI agents.
AI agents increasingly operate as autonomous or semi‑autonomous actors. They execute tasks, orchestrate workflows, and interact directly with enterprise systems and sensitive data. As agents move into live workloads, they start touching the everyday data, applications, and decision paths that employees create. That shift is exactly why centralized visibility and governance can’t wait.
The challenge now extends to control. Organizations are experimenting with different AI tools across multiple platforms — for example, teams collaborating in Microsoft 365 may still rely on Vertex AI agents built on Google Cloud. At this stage of adoption, visibility into agent behavior is a prerequisite. Without persistent, centralized insight, organizations cannot sustain trust, auditability, or confidence as agents assume greater responsibility.
Why Traditional Governance Models Break Down in Agentic, Multicloud Environments
Traditional AI governance models weren’t built for this moment: They assumed centralized models, predictable workflows, and control within a single cloud. Agentic AI breaks those assumptions as soon as agents begin operating autonomously across environments.
Multicloud environments now host a growing mix of first‑party, third‑party, and custom AI agents. These range from built‑in assistants on cloud and productivity platforms to vendor and in‑house agents built for specific workflows, each governed by different controls and assumptions. For example, one organization may use Microsoft 365 agents for collaboration, deploy third‑party analytics agents on Google Cloud, and run custom agents connected to internal systems, all with different visibility and security models. As cloud providers race to build their own agentic stacks, visibility fractures and risk become harder to assess holistically — a concern reflected in Gartner’s prediction that “loss of control” over AI agents will be a top issue for 40% of Fortune 1000 companies by 2028.
Gartner has identified AI governance platforms as a top strategic technology trend for 2025. However, in agentic, multicloud environments, governance must be designed from the start, through a single platform that inventories agents, surfaces risk, and enforces consistent oversight across the entire agentic stack.
That is the role AgentPulse is built to play.
AgentPulse: The Missing Layer of Multicloud Agent Visibility
While agent adoption is accelerating, only one in five organizations reports having a mature governance model for autonomous AI. The gap isn’t intent — it’s that most organizations still don’t have the foundations in place to support AI agents operating independently and at scale.
Confident AI adoption depends on visibility that keeps pace with scale. As agentic systems move into production and take on real responsibility, organizations need a clear understanding of how agents act and evolve across environments. When that visibility is built in, teams can expand agentic use cases with assurance rather than hesitation.
AgentPulse has expanded support for Google agents, including Vertex AI, to address this exact challenge. Rather than managing agents separately inside each platform’s native tools, it introduces an agent‑centric layer of visibility and governance that spans Microsoft, Google, and custom environments. Instead of checking separate admin consoles for Microsoft and Google, teams can see all active agents in one place — who created them, what they touch, and how they’re being used. Creation and usage trends across Microsoft 365 and Google Cloud add an operational view that reflects real‑world behavior over time.
These capabilities align visibility with value. Organizations that maintain clear oversight of their agentic AI systems are better positioned to scale them, meet compliance expectations, and demonstrate accountability as agent autonomy increases. With shared context across teams, decision‑making becomes faster and more consistent.
By reducing uncertainty and improving transparency, AgentPulse helps organizations move from cautious pilots to sustained adoption. Governance, then, becomes the foundation for long-term business value.
Ready to bring visibility and control to your agentic AI strategy?
Explore Agentic AI Governance and Visibility with AvePoint AgentPulse and see how centralized, agent‑centric governance can support confident AI adoption at scale.
