Building the Agentic Cloud

Hyperscale cloud providers possess decisive structural advantages for developing agentic-native infrastructure through a combination of vertical integration, enterprise positioning, and capital deployment capabilities that smaller providers cannot replicate. Unlike neoclouds that must abandon proven business models, hyperscalers can invest in agentic-native capabilities while maintaining existing revenue streams.

Enterprise relationship leverage provides competitive protection through existing customer trust and procurement integration. Rather than requiring organizations to evaluate entirely new infrastructure platforms, hyperscalers extend established infrastructure partnerships into agentic capabilities through trusted channels. Microsoft's integration of MCP across multiple enterprise platforms exemplifies this approach, while Google leverages Workspace relationships and Amazon builds on AWS enterprise trust.

However, these advantages face important constraints that may limit adaptation speed. Hyperscaler scale creates organizational inertia that can slow architectural transformation, particularly when existing infrastructure investments represent substantial sunk costs. Their diverse customer bases with varying requirements may limit their ability to optimize specifically for agentic workloads without compromising performance for traditional applications.

A Purpose-Built Infrastructure Solution

While hyperscalers possess structural advantages for comprehensive agentic infrastructure, the emerging agentic cloud provider demonstrates the performance improvements possible with an architecture designed intentionally to address agentic requirements, rather than retrofitted to existing platforms.

Memory management optimizes specifically for persistent context storage across agent sessions. Networking infrastructure supports the low-latency agent communication patterns that coordination protocols require. Resource allocation systems enable outcome-based pricing models that align infrastructure costs with successful task completion.

Daytona Cloud represents one example of this emerging category, addressing critical requirements that traditional cloud platforms handle poorly. Similar specialized providers are targeting specific components of the agentic infrastructure stack, including observability tools for multi-agent monitoring and optimization platforms for agentic workload management.

The emergence of these purpose-built providers highlights the performance gaps in traditional infrastructure while demonstrating substantial market demand for specialized agentic capabilities that traditional infrastructure providers typically cannot deliver efficiently without fundamental architectural redesign.

Economic Model Evolution

The shift toward agentic systems introduces changes in economic models.

Traditional cloud economics operate on consumption-based pricing where infrastructure costs correlate with resource utilization—compute hours, storage capacity, network bandwidth—regardless of task completion success or coordination efficiency. Agentic systems, however, enable outcome-based pricing that aligns infrastructure costs with successful task completion rather than resource consumption alone. This transformation demands new business models from providers, with success metrics focused on task completion rates and agent coordination efficiency rather than traditional uptime or throughput measurements. It also creates opportunities for higher margins through architectures optimized for agentic workflows that deliver measurable business outcomes.

This economic transformation shapes infrastructure design priorities. Providers must optimize for task success over raw resource utilization, balancing performance, redundancy, and cost efficiency in ways that traditional cloud economics do not reward.

Pricing models will favor providers who can demonstrate measurable improvements in agent coordination capabilities, fundamentally redefining competitive benchmarks for infrastructure performance in agentic environments.

Strategic Implications of Agentic-Native Cloud

The agentic shift presents a fundamental challenge: building infrastructure for a technology whose full potential remains uncertain. ChatGPT emerged just 2.5 years ago, yet the industry is racing to lay tracks for a train whose destination remains unclear but continues gathering unstoppable momentum.

With 33% of enterprise software projected to incorporate agentic AI by 2028 and autonomous agents predicted to handle 15% of daily work decisions, infrastructure requirements will expand across all dimensions of agentic architecture, and the capital allocation challenge compounds this complexity.

Providers must simultaneously fund uncertain agentic technologies while preserving existing revenue streams, balancing innovation acceleration with operational stability during an extended transition period. Hyperscalers benefit from diversified portfolios that provide investment flexibility, yet all infrastructure providers will face pressure to commit substantial resources without clear short-term returns.

Organizations that approach this transition as an incremental upgrade rather than foundational transformation risk displacement by competitors who recognize that the future belongs to platforms designed for intelligence, not merely processing power. The agentic era demands infrastructure that thinks, coordinates, and adapts to dynamic requirements while maintaining the reliability and scale that enterprise applications require.

The providers that successfully navigate this architectural transformation will establish dominant positions in the next phase of cloud computing evolution, while those that optimize for yesterday's requirements risk obsolescence in tomorrow's agentic landscape.

This article originally appeared in Decoding Discontinuity, a Substack blog.