Why Agentic AI Requires a Smarter Cloud Infrastructure

In today’s fast-moving business landscape, our customers are fundamentally reimagining how they engage with their own customers, leveraging agentic AI to create dynamic, responsive experiences.

This transformation, however, isn’t without its challenges. These AI systems must deliver on availability, performance, and scale, especially during peak and critical events. For enterprises that provide essential AI-powered services, the reliability of their systems is non-negotiable. As they embark on their journey to become an agentic enterprise, they require a trusted, robust infrastructure that can handle the unpredictable demands of AI, ensuring their business operates without a hitch. This is why our focus on engineering for AI scale is more critical than ever.

It’s not simply about throwing more compute at the problem; it’s about building a smarter, more dynamic, and inherently resilient foundation.

Beneath the surface of agentic AI systems lies a critical, often underestimated, challenge: the underlying cloud infrastructure. It’s not simply about throwing more compute at the problem; it’s about building a smarter, more dynamic, and inherently resilient foundation.

The Unique Workload Demands of Agentic AI

Unlike traditional enterprise applications with predictable usage patterns, agentic AI workloads are inherently unpredictable. Along with increased reasoning capabilities for more precise outcomes, they demand bursts of intense compute power, often requiring specialized hardware like GPUs, alongside rapid data ingress and egress. This burstiness, coupled with the need for ultra-low latency and high throughput, pushes conventional cloud infrastructure to its limits. Reactive scaling mechanisms, designed for more stable demand, fall short when faced with the volatile, diverse resource needs of intelligent agents.

To truly unlock the potential of agentic AI, we must move beyond mere elasticity and embrace an infrastructure that is intelligent, predictive, and highly efficient.

To truly unlock the potential of agentic AI, we must move beyond mere elasticity and embrace an infrastructure that is intelligent, predictive, and highly efficient. We need engineering systems that can anticipate demand, optimize resource allocation across a heterogeneous fleet, and enable rapid iteration for continuous improvement. This requires a fundamental reimagining of how we build and manage our cloud environments.

Engineering for AI Scale: Key Innovations

At Salesforce, we’re deeply invested in evolving our cloud infrastructure to meet these exacting demands. This includes developing and deploying innovations that directly address the unique characteristics of AI workloads:

• AI-Driven Predictive Autoscaling: Instead of reacting to current load, we’re leveraging AI itself to predict future demand. This allows our infrastructure to proactively provision and de-provision resources, minimizing latency, preventing cold starts, and ensuring optimal performance even during peak bursts. This intelligent foresight significantly enhances both user experience and cost efficiency.
• Intelligent Resource Management: Scaling isn’t just about adding more servers; it’s about allocating the right resources for the right task. Our intelligent resource management systems go beyond simple CPU and memory metrics, considering the specific requirements of diverse AI workloads — whether they need specialized GPUs, high-bandwidth memory, or particular input-output profiles. This granular optimization ensures that every computational dollar delivers maximum value.
• Accelerated CI/CD for AI: The pace of AI innovation is relentless. To keep up, our infrastructure must support rapid development, testing, and deployment of new AI models and underlying system changes. We’ve enhanced our continuous integration and continuous delivery (CI/CD) pipelines to be incredibly agile, enabling our engineers to iterate quickly and deploy improvements with confidence, ensuring our AI services are always on the cutting edge.

These infrastructure advancements translate directly into tangible benefits for CIOs, IT leaders, and engineers. They provide the confidence to handle unpredictable AI workloads, optimize operational costs without compromising performance, and accelerate the time to market for critical AI innovations. Businesses can focus on their strategic AI initiatives, knowing that the underlying plumbing is robust, reliable, and ready for whatever the future holds.

Hyperforce: The Foundation for Agentforce’s Scale

Our cloud infrastructure, Hyperforce, is the bedrock upon which these advancements are built. Designed for global scale, security, elasticity, and availability, Hyperforce provides the secure, compliant, and highly performant environment that agentic AI demands. The innovations in predictive autoscaling, intelligent resource management, and enhanced CI/CD aren’t just add-ons; they’re deeply integrated capabilities within the Hyperforce architecture.

This foundational strength allows Data Cloud to ingest, harmonize, and process vast amounts of enterprise data at scale, unlocking the full value of data across the enterprise and providing the critical context for AI. Hyperforce’s inherent scalability and intelligent resource orchestration empowers Agentforce, the digital labor platform, to operate with efficiency and reliability across the enterprise.

These advancements enable customers like 1-800Accountant to handle seasonal spikes. Agentforce autonomously resolved 70% of 1-800Accountant’s chat engagements during tax week in 2025.

Hyperforce doesn’t merely host Agentforce; it’s the essential enabler of its ability to scale, learn, and help companies transform into agentic enterprises.

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