AI agents do their best work when they can see the world as it is right now - current prices, fresh listings, live reviews - not a static export from last quarter or a spreadsheet someone copied manually. Giving AI agents live web data reliably has been the hard part.
Most AI agent platforms connect to the software teams already run - Gmail, Slack, Salesforce, Google Drive. Those integrations work well for structured data that lives in systems with proper APIs. But a lot of what agents actually need to act on isn't in those systems. It's on the live web: pricing pages, competitor listings, review aggregators, and professional tools that have no documented endpoint and no official way in.
Teams building on shared agent platforms like Houston are already solving this. Agents on these platforms get smarter collectively as people use and correct them - the learning belongs to the company, not to any one person's account. But those agents work best when they can pull live web data on demand. That's where Anakin comes in.
Here's what that looks like in practice.
What teams are building
These are two workflows already running on Houston - each one a task that previously required engineering time or manual effort most teams didn't have.
Pricing intelligence for hospitality
Yield management - tracking competitor rates and adjusting pricing in real time - is standard practice at large hotel chains. For independent hotel operators and Airbnb hosts, it has historically meant opening five browser tabs every morning, copying numbers into a spreadsheet, and doing it again tomorrow.
The agent changes that. It pulls current pricing, availability windows, review scores, and demand signals from Airbnb, Booking.com, Expedia, and Hostelworld, then writes everything to a Google Sheet. The revenue manager opens the sheet. The manual work is gone.
In a business where margins depend on pricing one step ahead of the market, this kind of daily intelligence used to require dedicated tooling or a data team. Now it's an agent any team can run.
Automotive pricing intelligence
A dealership with more than 1,000 employees needed the same thing for used cars. Pull current listings from AutoTrader, Cars.com, and a dozen competing platforms, aggregate them by make, model, trim, and mileage, and get that data to sales reps before a customer walks in and references what they saw online.
At that volume and frequency, browser-based scraping isn't practical. Wire - Anakin's network-level extraction tool - handles it by intercepting the background API calls that each site's own frontend makes to load its listings. No browser to spin up. No rendered page to parse. The data comes back structured, so the agent can use it directly.
How the connection works
For technical teams, the URL Scraper API is a direct HTTP call:
curl https://api.anakin.io/v1/url-scraper \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"url": "https://www.airbnb.com/s/Miami", "useBrowser": true}'The API is async - it returns a job ID, and you poll GET /v1/url-scraper/{id} for the result. The completed response includes clean markdown or JSON. Wire catalog actions use the same X-API-Key header, hitting a shared endpoint with a distinct action ID per catalog entry.
For non-technical teams - the default for most agent builders on platforms like Houston - the custom integrations panel is the entry point. Connect Anakin once. From that point, every shared agent in the workspace has access to URL Scraper, Wire catalog actions, Browser Sessions, Map, and Crawl. No API key per agent. No configuration per task. The person building the agent doesn't need to know how any of it works under the hood.
One MCP connection per workspace, and every agent in that workspace gets access from there. Teams connect Anakin once, and authorized Houston agents can reuse it across workflows. The agent instructions, and operational knowledge stay shared and company-owned. The non-technical path has to hold up when a room full of people is trying it for the first time. It does.
What Anakin adds to the mix
| Tool | What it does |
|---|---|
| URL Scraper | Fetches any URL - static or JS-rendered - and returns structured content |
| Wire | 940+ pre-built network-level scrapers for specific sites; scales without browser overhead |
| Browser Sessions | Saves authenticated session state so agents can reach behind-login pages, without handling passwords |
| Map + Crawl | Discovers a site's full URL structure, then aggregates content across every page |
Together, these give agents the same web access a human researcher has - automated, structured, and running at whatever cadence the workflow needs.
The ceiling most AI agent workflows hit
The hospitality case and the automotive case share the same structure: an agent needs to read something from the live web on a regular basis, that source doesn't offer a tidy integration, and without a solution the workflow stalls.
That's the ceiling most AI agent workflows quietly hit. The model is capable. The workspace is configured. The missing piece is the data.
A shared agent workspace handles how your team runs AI - corrections and skills accumulate, agents get smarter over time. Anakin handles what those agents can actually see: any site, any frequency, without needing to build or maintain the connection.
Get started
Add Anakin to your agent workspace through Houston's custom integrations panel. The MCP server connects in a single step - every agent gets access from there.
- Houston - gethouston.ai - free for up to three team members
- Anakin - anakin.io - 300 free credits included to get started