Scaling Agentic AI Demands Integrated Governance and Infrastructure
What does it take to scale Agentic AI responsibly? At Google Cloud Next, the message was clear: autonomous agents are moving from experimentation to production fast. And walking the IBM booth highlighted a more grounded reality: agentic scale is not just an AI challenge; it’s a governance and infrastructure challenge. I break it down into 5 layers that need to work together: 1️⃣ Financial Layer - Can you afford to scale? Apptio, an IBM Company brings real-time FinOps discipline; allocating spend, detecting anomalies early, and preventing agentic sprawl before it starts. 2️⃣ Resource Layer - Are your agents properly equipped? IBM Turbonomic reframes right-sizing as a performance and reliability issue rather than just cost optimization. 3️⃣ Automation Layer - Who governs the infrastructure itself? HashiCorp Terraform, combined with IBM Turbonomic, enables autonomous optimization with traceability and compliance. 4️⃣ Partnership Layer - Platform + Governance Google Cloud provides the AI and cloud-native foundation. IBM brings the operational governance layer needed to scale it responsibly. 5️⃣ Practical Layer - What leaders are already feeling Runaway cloud costs. Infrastructure drift. Agents deployed without lifecycle discipline. This is where IBM’s portfolio shifts from product story → operational necessity. The gap between AI ambition and enterprise reality is narrowing; but not automatically. Follow along as I sit down with Alan Bivens, VP of Strategic Partnership at IBM to go deeper on how enterprises are closing this gap. What’s the biggest blocker you’re seeing to scaling agentic AI responsibly today? To stay current with the latest trends in and, Subscribe to 👉 here https://lnkd.in/gy2RJ9xg or 👉 here https://lnkd.in/gnMc-Vpj
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