Parag Agrawal’s Parallel Web Systems raises $100 million at $2 billion valuation

Parag Agrawal’s Parallel Web Systems has raised $100 million in fresh funding at a $2 billion valuation, a leap that puts the former Twitter chief’s startup at the center of Silicon Valley’s latest race: building the plumbing for AI agents. Sequoia Capital led the Series B, and partner Andrew Reed is joining Parallel’s board. The company said the round brought total funding to $230 million.

The new valuation more than doubles Parallel’s $740 million Series A price from November 2025, underscoring how aggressively investors are bidding up companies that claim to solve one of generative AI’s most immediate bottlenecks. Parallel says it builds APIs and web infrastructure that let AI agents access the live web, a capability that matters for tools expected to pull current information, navigate websites, and complete tasks in real time rather than rely only on static model training data.

Parallel says enterprise customers are already using its systems for coding, sales data analysis and insurance risk underwriting, with Harvey, Notion, Profound, Actively and Opendoor among its users. The company says demand has accelerated since its public launch in August 2025, after it was founded in 2023 and first raised $30 million in January 2024. The round also included increased participation from existing backers Kleiner Perkins, Index Ventures, Khosla Ventures, First Round Capital, Spark Capital, Terrain Capital and Abstract Ventures.

Agrawal said the new capital will go toward sales and marketing expansion, continued research and development, and customer acquisition. He also said Parallel is pursuing an “open market mechanism” designed to compensate publishers while keeping web content accessible to AI systems, a sign that the company sees access to data as both a technical and commercial problem.

The deal reflects a broader shift in AI capital markets. Investors are moving beyond base model training and into the infrastructure layer that could make AI agents useful inside companies, from search to underwriting to workflow automation. The question for the market is whether that demand is durable enough to justify billion-dollar pricing this early, or whether funding is outrunning proof that agents can deliver reliable, repeatable business value at scale.