Most AI agents are using the slowest possible API

Most AI agent stacks today reach the internet by acting like a person. Spin up a sandbox. Spin up a browser inside the sandbox. Load the page. Wait. Scroll. Wait. Take a screenshot. Pipe HTML into the model context. Parse the answer out. Move on.

It works. It is also the slowest possible way to do this.

We have been timing it. In one cold Zillow listing fetch on April 3, 2026, Everfetch completed first and returned 41 structured listing records using about 2.2k agent tokens. Firecrawl returned page markdown with about 9 visible listings using about 7.1k agent tokens. That is 3.2x fewer agent tokens and about 3.2x lower model context cost when both outputs use the same model token rate. This is a narrow workflow result, not a general speed, reliability, or total cost claim.

Benchmark method and limits

Both requests ran from the same machine and network against the same San Francisco listing page. Everfetch ran with browser use and cache disabled. Firecrawl ran in markdown mode with timing enabled. The timer covered each command from start to response completion. Record counts came from the returned payloads. Agent token counts use the recorded model ready values from the public benchmark source. The tokenizer configuration was not retained in this summary. We did not repeat the run enough times to report a distribution, and the two outputs did not have the same shape. The result shows what happened for this task on this date only.

The machine readable benchmark record includes the timestamp, method, input URL, returned record counts, agent token counts, and limitations. It omits internal timing values. The model context cost comparison assumes the same model token rate and excludes tool fees, infrastructure, and every other workflow cost.

That is the end-to-end workflow number. The schema lookup path is faster still. In a 12-sample check across four registry entries:

Those are registry package lookups, not full browser replacement benchmarks. The important point is that the schema layer is not the bottleneck.

The reason for the gap is not clever engineering on our end. It is that the browser is doing work nobody needs.

The structured data was already there

When you load Zillow.com, you do not really see HTML. You see an HTML shell that loads JavaScript and renders the page from structured data.

That same kind of structured data sits behind search boxes, product pages, review sections, fare grids, listing pages, and government lookup tools. It is faster than the HTML, returns cleaner data, and your agent's model did not need the surrounding markup anyway.

The structured data has been there the whole time. The industry just decided to ignore it.

Why everyone got stuck on browsers

Browsers solved real problems. JavaScript rendering, dynamic pages, and “it works on every site” all matter. If you need data from one page, the browser is often the smallest reproducible unit.

But agents are not loading one page. They fan out across dozens of sources, run around the clock, and pay tokens for every byte of HTML they never needed. At that point the browser stops being a feature and starts being a tax.

Everfetch is a registry of website APIs. We document them as schemas, you call them through our runtime, and your agent gets clean JSON back. No extra browser loop, no page parsing, no scraping logic in your agent.

What this looks like for actual companies

The teams most likely to feel this pain are vertical AI companies, where data quality is the product. A few categories show the pattern clearly:

• Sales and outbound AI. Outbound tools enrich leads at thousands of records per workflow. Browser automation gets fragile at that volume. Structured APIs are a better fit.
• Travel AI. Trip-planning products pull airline and hotel inventory in real time. A booking concierge that takes ten seconds to find a fare has already lost to one that takes one.
• Shopping AI. Agent-driven commerce products face the same constraint across more retailers.
• Legal and compliance AI. Regtech products need federal, state, and international source coverage. The registry already covers sources like Congress, CDC, IRS, NASA, EU Lex, and OpenStates.
• Finance and on-chain analytics. On-chain analytics and equity research teams depend on structured source coverage. Etherscan alone has dozens of useful endpoints.
• Social listening and consumer insights. The data surface that hedge funds and brand teams used to buy from listening platforms is becoming agent infrastructure, delivered as one fetch instead of another dashboard subscription.
• General research and analyst agents. Research and analyst systems fan out across all of the above on a single user question.

If your product touches any of these categories and you are operating a browser heavy scraping stack, test one repeated workflow against both approaches and compare usable records, latency, parsing work, reliability, and total cost.

How it works

A schema is a plain-text package describing how to request and normalize one site's structured data: available actions, parameters, auth requirements, response shape, and expected output. Your agent does this:

GET  https://api.everfetch.tech/v1/schemas/{site}/package
POST https://api.everfetch.tech/v1/fetch

Everfetch handles request execution, user provided auth where required, and response normalization behind a stable API. You pay per fetch. Compare the full workflow against your current approach using the records, latency, parsing work, reliability, and total cost that matter to your product.

Schemas are designed to be inspectable and portable. The runtime is hosted by us. You do not operate any of it.

Try it

• Drop the client into your stack. npx skills add everfetch-ai/skills --skill everfetch installs Everfetch into agent harnesses that support skills. Sign up at everfetch.tech for an API key.
• Run the API directly. POST https://api.everfetch.tech/v1/fetch with your most painful current scraping workflow. We will publish a side-by-side benchmark if it is one we cover.
• Talk to us about scale. If you are a vertical AI company and want to discuss custom schemas, dedicated infrastructure, or enterprise SLAs, email hello@everfetch.tech.

The structured data has always been there. We just made it callable.