The global cloud infrastructure stack is being restructured around agentic AI workloads, according to Omdia’s 2026 Global AI Cloud Stack Analysis reported by SquaredTech. The research firm identifies multi-agent orchestration and real-time inference as the two most critical pressure points for cloud providers, representing structural gaps between what current infrastructure was designed to do and what autonomous agent workloads demand.
Why Agent Workloads Break Traditional Cloud
Standard cloud AI infrastructure was optimized for stateless, high-throughput inference: send a request, get a response. Agentic workloads are fundamentally different. They are stateful, long-running, and deeply interconnected. A single agent task can spin up multiple model calls, query vector databases, write to external APIs, and loop back on itself dozens of times before completing, as SquaredTech reports.
That behavioral shift demands persistent state management, multi-model coordination, low-latency inference at scale, and granular observability across non-linear workflows. Storage must be optimized for vector retrieval, networking for low-latency inter-agent communication, and compute for inference rather than training.
Three Hyperscalers, Three Strategies
AWS, Microsoft Azure, and Google Cloud are each taking distinct approaches, according to the SquaredTech analysis of Omdia’s findings.
Amazon is building on Bedrock as a managed foundation for multi-agent systems, adding agent collaboration features and tighter integration with its data and storage services. The pitch: enterprises already on AWS should not need to build their own orchestration layer.
Microsoft is leveraging its OpenAI partnership to push Copilot Studio and Azure AI Foundry as enterprise-grade agent environments. Its advantage is the massive existing Microsoft 365 customer base, making it a natural entry point for agentic tools that plug into existing workflows.
Google Cloud is making what the analysis describes as “the most technically ambitious bet” with Vertex AI Agent Builder and the Gemini model family. Google’s combination of frontier models, proprietary TPU infrastructure, and distributed systems expertise targets the exact requirements that complex, stateful agent workloads create.
Enterprise Obstacles
For organizations deploying agentic systems today, three operational problems dominate, per the SquaredTech report.
Latency compounds across multi-step chains. When an agent workflow sequences multiple model calls, retrieval steps, and API interactions, even small delays at each step make end-to-end performance impractical for production use.
Cost scales faster than expected. Agentic workloads generate far more API calls, consume more tokens, and run for longer durations than traditional AI tasks. Standard cloud cost management tools were not built for multi-agent pipelines, driving demand for per-agent cost attribution. A new wave of AI observability startups, including Langfuse, Arize AI, and Weights & Biases, are building specifically for this gap.
Governance remains unsolved. When agents can send emails, modify databases, place orders, and trigger workflows, the question of accountability becomes urgent. Regulatory frameworks are not keeping pace, and most enterprises are building guardrails from scratch.
The Infrastructure Reshuffling
Omdia’s findings point to something larger than a new workload category, according to the analysis. The agentic shift is rearchitecting assumptions that have underpinned cloud infrastructure for over a decade. Procurement decisions, partnership strategies, and product roadmaps across the industry are all being reshaped by the requirements of autonomous agent systems. None of the three hyperscalers holds a decisive lead, which is precisely why the competition is intensifying now.