AWS Redesigns OpenSearch Serverless for AI Agents: Decoupled Compute & Storage to Handle Machine-Dri

Cloud Infrastructure has long been designed around humans who search, CLIck, scroll, and stream in a steady, predictable fashion. AI Agents behave differently. They can unleash a swell of ACTivity — spinning up multiple sub‑Agents that query hundreds of databases, search documents, and call APIs within SEConds — and then disAPPear as quickly as they ARRived.

Under that premise, Amazon is redesigning a core piece of its cloud infrastructure. On Thursday, AWS launched its next generation of OpenSearch Serverless, a fully managed search and vector database (a system for storing and retrieving Information at scale) that is designed specifically for agentic workloads. AWS says the new system can instantly scale up when agents trigger tasks and scale back down to zero when idle.

This launch reflects a growing realization across the tech industry: infrastructure originally built for a human‑driven internet does not work as well in a world increasingly populated by Autonomous Agents.

While AI agents still represent a relatively small portion of overall internet activity, machine‑generated traffic is already significant — and poised to grow. Cloudflare reports that bots accounted for 31% of global HTTP traffic over the last six months. AI crawlers, search engines, and assistants made up roughly one‑quarter of all bot requests during that period.

“Non‑human traffic will exceed human traffic sometime in the first half of 2027,” Lai Yi Ohlsen, Senior Product Manager at Cloudflare, told TechCrunch.

At Google’s I/O developer conference last week, the company said users will soon be able to delegate tasks to AI systems — such as researching purchases, booking travel, browsing the web, and interacting with apps. But the shift does not stop at consumer‑focused agents. Enterprises are increasingly deploying agents internally and for their customers, creating new forms of machine‑generated traffic behind the scenes.

As a result, cloud providers and infrastructure companies have been rethinking how to adapt systems built for humans to a world of agents that constantly and autonomously retrieve information, invoke tools, and generate machine‑to‑Machine Traffic.

That is where AWS’s new OpenSearch Serverless comes into play.

“The timing is straightforward. Agents are moving from experimentation into production, and they create traffic patterns that previous infrastructure simply wasn’t designed for,” Tia white, General Manager for Amazon OpenSearch Service, told TechCrunch. “They spike without warning, they go idle without notice, and enterprise needs search that keeps up without paying for empty or idle compute.”

The key technical change in this new generation is that it decouples compute from stoRAGe. This allows compute to scale up in seconds to accommodate agent traffic bursts and to scale down to zero, so customers pay $0 when agents are idle.

“Previously, even in our prior Serverless veRSIon, you had to have at least one instance Operational and running because storage and compute were coupled,” White said. “You couldn’t just automatically spin up [compute] at the rate you needed, so you always had idle compute reserved for your workload — whether you were using it or not.”

Think of it like always paying for a parking space even when you are not using it. With AWS’s upgraded Serverless, it becomes more like paying for a metered parking spot.

At launch, OpenSearch Serverless will integrate natively with AI Development platforms such as Vercel and Kiro, allowing developers to deploy production‑ready search and vector backends for agents without manAGIng infrastructure.

This shift is emerging across the cloud industry. Databricks and Snowflake are repositioning themselves as AI mEMOry and retrieval systems for enterprise data. Microsoft has rolled out updates to Azure designed to handle AI agent bursts and share memory between agents. Cloudflare — in a similar vein to Amazon — last month introduced infrastructure aimed at giving agents persistent environments and instant scalability.

The more companies deploy AI Agents, the greater the pressure to redesign infrastructure around machine‑generated workloads. That, in turn, could make agents cheaper and easier to deploy at larger scales.