Book restaurant reservations end to end on real sites
The poster built agents that find a restaurant, fill out the reservation form and confirm the booking on a real site, handling dynamic elements and bot detection. Task: Suggested implementation: An agent workflow designed to locate restaurants, fill reservation forms, and confirm bookings on live websites. The proposed stack includes stealth browsers with persistent profiles, residential proxies, and CAPTCHA-solving services. Human involvement is required for initial manual setup and configuration of these separate components.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include manual configuration of stealth browsers, proxies, and CAPTCHA solvers, as the stack is not a plug-and-play solution. Integration uncertainty is high due to the 'duct-taped' nature of the components. Source reliability is low as the original URL returned a 403 error, preventing verification of specific technical details..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
The poster built agents that find a restaurant, fill out the reservation form and confirm the booking on a real site, handling dynamic elements and bot detection.
Workflow
Suggested implementation: An agent workflow designed to locate restaurants, fill reservation forms, and confirm bookings on live websites. The proposed stack includes stealth browsers with persistent profiles, residential proxies, and CAPTCHA-solving services. Human involvement is required for initial manual setup and configuration of these separate components.
Reported result
Outcome unknown due to inaccessible source. The original claim stated the flow worked end-to-end on real sites, handling dynamic elements and bot detection, but explicitly avoided payment processing. No verified metrics or success rates are available from the provided data.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include manual configuration of stealth browsers, proxies, and CAPTCHA solvers, as the stack is not a plug-and-play solution. Integration uncertainty is high due to the 'duct-taped' nature of the components. Source reliability is low as the original URL returned a 403 error, preventing verification of specific technical details.
Try it
Act as an automation engineer. I need to build a proof-of-concept for a restaurant reservation agent. [Replaceable inputs: Target restaurant website URL, reservation date, time, party size, contact details]. Steps: 1. Analyze the target site's reservation form structure. 2. Configure a headless browser with stealth plugins to bypass basic bot detection. 3. Implement a script to fill the form fields with the provided inputs. 4. Execute the submission in a dry-run mode first to verify field mapping without sending real data. 5. If successful, perform a single live test on a low-risk site. Deliverables: A Python script using Playwright or Puppeteer, and a log of the dry-run results. Quality Check: Ensure no payment triggers are activated. Confirm form fields match inputs exactly. Error Handling: If CAPTCHA appears, pause and request human intervention. If the site blocks the request, log the error and stop. Note: This is a suggested workflow based on unverified claims. Do not assume this will work on high-security sites without further testing.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.