Tensorlake Inc. says it’s making life easier for organizations that want to design, build and run artificial intelligence agents with the debut of its new serverless infrastructure platform, which provides a ready-made foundation for autonomous systems to scale up.

The startup says it’s trying to tackle the overwhelming “infrastructure sprawl” that becomes almost inevitable when enterprises try to integrate autonomous AI agents into their business workflows. Doing so requires companies to stitch together all kinds of compute, orchestration, queuing, document processing and sandboxing tools and create complex data pipelines with governance structures and everything else.

Tensorlake wants to help organizations to avoid all of those hassles and swap out multiple infrastructure components for a single, all-in-one agentic platform that transforms unstructured data into production-ready inputs for AI. “AI agents are becoming pervasive because modern models can reason and take action, but the infrastructure to run them reliably hasn’t kept up,” said founder and Chief Executive Diptanu Gon Choudhury. “We built Tensorlake so teams can focus on business logic in plain code while the platform handles durability, scale, security and data access automatically.”

Choudhury said the company’s infrastructure can be likened to a “batteries-included” platform for building agentic AI workflows. It integrates a native request queuing capability to support bursty, high-throughput traffic, autoscaling container infrastructure that scales dynamically as demand increases, and secure sandboxes for running untrusted code inside virtual machines.

Other components include a durable execution engine that allows agents to survive system crashes and pick up where they left off without losing context about what they were doing previously, and application programming interfaces for extracting and structuring data and insights buried within PDFs, handwritten documents and charts.

Combined, these components create a developer-friendly infrastructure that allows teams to create agentic workflows and AI agents in Python, deploy them as APIs and scale them as and when needed, with built-in observability functions. The APIs are compatible with coding agents such as Claude Code, which means teams don’t even have to build the deployment workflows either – instead, everything can be automated.

Holger Mueller of Constellation Research said the debut of Tensorlake shows that the AI industry is already moving on from last year’s focus on data architectures. “This year it’s the battle of AI frameworks, and of course it’s the startups that are doing some of the most innovative things, with Tensorlake making a very strong debut,” the analyst said. “With the founder’s personal experience of deciding the best workload location, Tensorlake brings important qualities to agentic frameworks, such as elasticity, durability and portability.”

Tensorlake’s infrastructure can be deployed across Amazon Web Services, Microsoft Azure and Google Cloud environments, Choudhury added. According to him, it’s a unified system for building complete agent workflows that goes beyond reading data to enable automation at enterprise scales.

The startup, which has raised $8.6 million in funding so far, said its platform is already being used by customers such as the multinational car rental firm Sixt SE, as well as a number of major utilities providers and financial institutions in the U.S.

Images: Tensorlake