Parag Agrawal's Parallel raises from Sequoia as AI agents drive explosive web infrastructure demand

Parag Agrawal is building Parallel Web Systems around a bet that AI agents will consume the web at a scale that makes current infrastructure look inadequate. The company's pitch is straightforward: any product powered by an LLM needs live, comprehensive web access, and Parallel is building the stack to provide it.

Andrew Reed of Sequoia Capital is joining Parallel's board, signaling a new funding round. No amount was disclosed.

What the company actually does

Parallel sits between AI agents and the web, handling crawling, indexing, ranking, and serving content in formats agents can actually use. Agrawal frames it as a vertically integrated infrastructure layer, analogous to AWS exposing everything from raw VMs to serverless compute. The product suite includes APIs, MCP servers, and CLIs, each targeting different customer needs.

A recurring challenge is that much of the web was built for humans across very different eras, and agents need to access all of it. Parallel translates legacy web content into agent-readable formats, and has also been working with publishers and content owners to align their incentives around AI access rather than blocking it. The company has been live with its product since August 2025.

“I don't know how many agents you use, but like this year, the breadth of customers, the quality of customers, the variety of use cases, they've all exploded well beyond our wildest imaginations... Agents are gonna use the web a thousand times more than humans — and it's probably an undershooting of the number.”

Demand arriving faster than expected

Agrawal says that six months ago, AI agents using the web was still an abstract concept. Now the breadth of customers, use cases, and deployment quality has "exploded well beyond our wildest imaginations." Reed corroborates this from the Sequoia side, describing repeated patterns across portfolio companies independently adopting the same product, combined with pipeline visibility into what those companies plan to ship for the rest of 2025.

Reed frames the core shift as agents moving from running locally with limited access to running continuously in the background with a full workspace, deeply overlapping with knowledge work. Agrawal had originally argued that agents would use the web a thousand times more than humans. Reed now suggests that number is probably an undershot.

Web infrastructure strain is already visible. Major pieces of web infrastructure are hitting uptime problems from the surge in agent-driven demand, with shortages showing up in CPUs and other hardware.

Content access as the moat

Agrawal ties Parallel's longer-term positioning to a thread that runs from his time at Twitter: keeping more content open and accessible rather than locked behind walls. As publishers grow anxious about agents scraping their content without compensation, some are moving to block access. Parallel is working on the other side of that problem, building mechanisms for content owners to share in the value AI creates from their material. Agrawal says more detail on that side of the business is coming.

Reed draws a parallel to Robinhood, which appeared to be a commission-free trading app but had quietly built the first self-clearing system in the US in roughly 30 years. The deeper infrastructure Parallel is building, designed for scalability, reliability, speed, and cost at the scale agents will require a year or two from now, is similarly not yet visible to the market.