My team and I are building Tabstack to handle the web layer for AI agents. Today we are sharing Tabstack Research, an API for multi-step web discovery and synthesis.

https://tabstack.ai/blog/tabstack-research-verified-answers

In many agent systems, there is a clear distinction between extracting structured data from a single page and answering a question that requires reading across many sources. The first case is fairly well served today. The second usually is not.

Most teams handle research by combining search, scraping, and summarization. This becomes brittle and expensive at scale. You end up managing browser orchestration, moving large amounts of raw text just to extract a few claims, and writing custom logic to check if a question was actually answered.

We built Tabstack Research to move this reasoning loop into the infrastructure layer. You send a goal, and the system:

- Decomposes it into targeted sub-questions to hit different data silos.

- Navigates the web using fetches or browser automation as needed.

- Extracts and verifies claims before synthesis to keep the context window focused on signal.

- Checks coverage against the original intent and pivots if it detects information gaps.

For example, if a search for enterprise policies identifies that data is fragmented across multiple sub-services (like Teams data living in SharePoint), the engine detects that gap and automatically pivots to find the missing documentation.

The goal is to return something an application can rely on directly: a structured object with inline citations and direct links to the source text, rather than a list of links or a black-box summary.

The blog post linked above goes into more detail on the engine architecture and the technical challenges of scaling agentic browsing.

We have a free tier that includes 50,000 credits per month so you can test it without a credit card: https://console.tabstack.ai/signup

I would love to get your feedback on the approach and answer any questions about the stack.