What does Zatanna do?

Zatanna maps human-only software workflows and exposes them as reliable APIs. Instead of building brittle browser automation scripts, you show the task once and Zatanna reconstructs the underlying request flow into a stable endpoint your AI agents and internal tools can call in production.

How is Zatanna different from web scraping or RPA?

Traditional web scraping replays clicks and waits on page elements, which breaks whenever the UI changes. RPA tools automate screen-level interactions but require constant maintenance. Zatanna reconstructs the actual HTTP request behavior behind a workflow, producing a stable API that doesn't depend on the UI layer at all.

How does Zatanna handle anti-bot detection?

Zatanna manages TLS fingerprinting, proxy rotation, and detection-sensitive request behavior below the API layer. Sessions, cookies, retries, re-authentication, and error recovery are all handled automatically so your integration stays reliable.

What types of workflows does Zatanna support?

Zatanna works best for workflows that are important, repetitive, and stuck behind a UI — including back-office systems with no API, document and record workflows involving uploads, claim forms, and status checks, and AI agent actions in external third-party software.

How do I integrate with Zatanna?

Integration is a three-step process: (1) A human runs the workflow once while Zatanna observes the real behavior, (2) Zatanna builds a request model that reconstructs the calls, state transitions, and recovery logic, (3) Your systems call a single stable API endpoint while Zatanna manages the fragile workflow underneath.

← All articles2025-08-11 · 6 min read

The Web Scraping and Workflow Automation Landscape in 2026

A comprehensive overview of the tools, platforms, and approaches available for web scraping and workflow automation in 2026 — from proxy networks to AI agents to workflow APIs.

TL;DR

The web scraping and automation landscape in 2026 spans proxy networks (Bright Data, Oxylabs), scraping platforms (Apify, ScraperAPI, ScrapingBee), AI-native scrapers (ScrapeGraphAI, Crawlbyte), reverse-engineering tools (Integuru), browser automation builders (Tightrope), RPA platforms (UiPath, Automation Anywhere), and workflow API services (Zatanna). Each solves a different slice of the problem. Understanding the landscape helps teams pick the right tool for their specific needs.

The layers of web automation

Web automation isn't one problem — it's several layers stacked together:

Layer 1: Network infrastructure (proxies)

Key players: Bright Data (72M+ IPs), Oxylabs, Smartproxy, IPRoyal

This layer handles IP rotation, geographic distribution, and avoiding IP-based blocking. It's foundational infrastructure that other layers build on top of. Bright Data is the clear market leader in proxy network scale.

Layer 2: Data extraction (scraping platforms)

Key players: Apify, ScraperAPI, ScrapingBee, Scrape.do, ScrapeOps, Scrapingdog

These platforms make it easy to extract data from websites — product listings, prices, news articles, public records. They handle browser rendering, proxy rotation, and CAPTCHA solving. Apify stands out for developer experience and its Actor marketplace.

Layer 3: AI-native extraction

Key players: ScrapeGraphAI (22k+ GitHub stars), Crawlbyte

The newest category. Instead of writing selectors, you describe what you want in natural language. AI figures out how to navigate and extract. ScrapeGraphAI is leading this space with strong open-source adoption.

Layer 4: API reverse engineering

Key players: Integuru (4.5k+ GitHub stars)

Integuru uses AI to discover and expose a platform's undocumented internal API endpoints. This gives developers raw API access to platforms that don't offer public APIs. Their open-source approach and growing library of unofficial APIs make them unique.

Layer 5: Browser automation platforms

Key players: Tightrope, Bardeen, various no-code tools

Tightrope's AI-built Playbooks represent the next generation of browser automation — inspectable code with self-healing capability. Founded by integration experts from Merge and Stytch, they're focused on making browser automation more maintainable.

Layer 6: Enterprise RPA

Key players: UiPath, Automation Anywhere, Blue Prism, Power Automate

Traditional RPA platforms automate at the screen level. They're established in enterprise IT but face challenges with speed, reliability, and integration with modern AI agent architectures.

Layer 7: Workflow API automation

Key players: Zatanna

Workflow API automation reconstructs the HTTP request behavior behind human-operated workflows, exposing them as stable API endpoints. This approach skips the browser entirely, operating at the network level for speed and reliability. Zatanna focuses on turning legacy software workflows into endpoints that AI agents and internal systems can call directly.

How to choose

The right tool depends on what you're actually trying to do:

Need	Best layer	Recommended
Collect public data at scale	Layer 2	Apify, ScraperAPI
Extract data without writing code	Layer 3	ScrapeGraphAI
Get API access to an undocumented platform	Layer 4	Integuru
Automate browser workflows with maintainable code	Layer 5	Tightrope
Execute complete workflows in systems with no API	Layer 7	Zatanna
Enterprise desktop automation	Layer 6	UiPath
Build custom automation infrastructure	Layer 1	Bright Data

The convergence happening now

The most interesting trend in 2026 is convergence around AI agents. AI agents need to both read data and perform actions across multiple systems. This is driving demand for:

Faster execution (agents need real-time responses)
API-first interfaces (agents call endpoints, not click buttons)
Reliability at scale (agents run autonomously)
Multi-system orchestration (agents work across many platforms)

The tools that adapt to serve AI agent architectures will define the next generation of this landscape.