AI Native Infrastructure

The public cloud as we know it was built for the web. Platforms like AWS, GCP, Azure, and even newer entrants like Cloudflare have all evolved around the same fundamental assumptions: workloads are stateless, requests are short-lived, and scale is achieved by horizontally distributing those requests across thousands of small, disposable workers. This model has proven incredibly effective for websites, APIs, and mobile backends. But as soon as you introduce AI agents into the picture, the cracks start to show. Agents are not websites. They are not APIs. They are not disposable stateless workers. They are long-running, stateful, adaptive systems, and trying to force them into the mold of web-native infrastructure exposes every limitation of the hyperscalers' design.

Why Hyperscalers Struggle with Agents

Traditional serverless platforms demonstrate this mismatch most clearly. AWS Lambda, Google Cloud Functions, and Azure Functions were all built with a maximum execution time measured in minutes. This design choice makes sense for quick tasks like resizing images, transforming data, or responding to API calls. But an agent tasked with synthesizing research, coordinating with multiple external APIs, and iterating on its own reasoning does not complete in 30 seconds. In fact, it may not complete in 30 minutes. Many meaningful agent workloads are open-ended, running for hours or in some cases indefinitely. Imposing arbitrary timeouts makes them impossible to run effectively. Developers are left with an ugly choice: either re-architect the agent into a set of brittle micro-jobs that constantly checkpoint and restart, or abandon the hyperscaler platform altogether. Neither option is sustainable if agents are to become mainstream.

There is another structural problem. The web is designed to be stateless. Each request is treated as independent, carrying all of the information it needs to be processed. State, if it exists at all, is pushed into databases or caches. Agents do not work this way. They maintain context, carry memory across interactions, and build on previous reasoning. They cannot simply forget everything when the next request arrives. This makes them fundamentally stateful, and infrastructure designed around statelessness will always be fighting against their nature. Attempting to retrofit persistent state into environments that were never built for it inevitably results in complexity, unreliability, and cost inefficiency.

Rick Blalock, co-founder of Agentuity, captured this tension when reflecting on his own experience trying to run agents in serverless environments:

“You build this thing, you deploy it on serverless—say AWS Lambda—and then you hit timeouts. Your agent runs for 15 minutes, 30 minutes. We even have an internal agent that runs for 40 minutes a day. And then you realize, ah crap, I have to re-architect the whole thing because it’s not going to work.”

That story has been repeated countless times by teams experimenting with agents. What feels like a promising proof of concept quickly runs into infrastructure ceilings that were never designed with agent workloads in mind.

What Agents Actually Require

Beyond execution time and state, there are deeper qualities agents demand that web-native platforms cannot offer. Agents need to pause and resume naturally, because their work is not always linear or bounded. They need to handle diverse forms of input and output, because not every interaction is a simple HTTP request and response. They need to reason about their own performance, not just emit logs for humans to parse, but to carry self-observation and self-reflection inside their own loop—the kind of complex agent workflows that require sophisticated orchestration. They need to communicate with other agents as peers, forming networks of collaboration rather than isolated microservices. These are not exotic requirements. They are the everyday realities of working with intelligent, autonomous systems. Yet none of them map cleanly to infrastructure optimized for ecommerce websites and API gateways.

This is why trying to retrofit agents onto existing hyperscaler platforms feels like working uphill. The primitives simply do not exist. Developers end up fighting the infrastructure, bending their design to fit platform limitations, and losing velocity along the way—which is precisely why building AI agents presents such unique challenges compared to traditional software development.

The Case for AI Native Infrastructure

The necessary shift is toward AI native infrastructure—cloud environments designed from the ground up with agents in mind. That means agents are not treated as second-class workloads shoehorned into serverless or VM abstractions. They are first-class citizens, routable, observable, composable, and extensible at the infrastructure layer. Deploying an agent should feel natural, not like a hack. Developers should be able to run agents for as long as needed, persist memory across sessions, connect them securely to each other, and inspect their reasoning with the same ease that a web developer inspects logs.

The hyperscalers will continue to dominate for web-scale workloads. Their platforms are extraordinary at what they were built to do. But that same optimization makes them fundamentally ill-suited for agents. The more they double down on serverless abstractions, the harder it becomes to accommodate systems that are long-running, stateful, and adaptive. AI native infrastructure fills this gap. It is not an incremental improvement or a clever workaround. It is a rethinking of what a cloud platform should offer when the primary workload is no longer serving web pages or APIs, but hosting autonomous systems that observe, reason, and act.

Looking Forward

The future of software depends on infrastructure catching up with the reality of agents. Just as the web demanded its own generation of infrastructure—CDNs, autoscaling containers, serverless runtimes—the agent era demands its own. Autonomy, proactivity, and goal-directed behavior cannot thrive when forced into an environment that was built for something else. AI native infrastructure is the foundation that will allow agents to move from prototypes to production, from experiments to everyday coworkers. Without it, we are only scratching the surface of what agents can do.