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 articles2026-03-05 · 4 min read

RPA vs. API-Based Automation: Which Approach Actually Scales?

RPA automates screen-level interactions and requires constant maintenance. API-based automation reconstructs the underlying system calls for reliable, scalable workflows that AI agents can call directly.

TL;DR

RPA (Robotic Process Automation) tools like UiPath, Automation Anywhere, and Blue Prism automate by recording and replaying screen-level interactions. They work well for simple desktop tasks but struggle with scale, reliability, and integration with modern AI systems. API-based automation reconstructs the actual system calls behind a workflow, producing endpoints that are faster, more reliable, and directly callable by AI agents.

How RPA works

RPA bots mimic human actions — clicking buttons, typing into fields, reading text from screens. They operate at the UI layer, interacting with applications the same way a person would. This makes them easy to set up for simple tasks: copy data from one system, paste it into another, click submit.

The appeal is clear: no code required, works with any application that has a screen, and business users can build automations themselves.

Where RPA falls short

The problems emerge at scale. RPA bots are inherently fragile because they depend on visual elements staying exactly where they were when the automation was recorded. Common failure modes include:

UI changes — a button moves, a field gets renamed, a dialog box appears
Performance — bots run at human speed because they interact through the UI
Concurrency — most RPA tools struggle with parallel execution
Error handling — when something unexpected happens, bots either crash or require human intervention
AI integration — RPA bots aren't APIs, so connecting them to AI agents requires additional middleware

Organizations running RPA at scale typically spend 30-50% of their automation budget on maintenance rather than building new automations.

How API-based automation works

API-based automation takes a fundamentally different approach. Instead of automating the screen, it reconstructs the actual HTTP requests and system calls that happen when a human performs a task. The result is a clean API endpoint — fast, reliable, and directly callable.

Zatanna's approach:

1. A human demonstrates the workflow once in the actual system

2. Zatanna captures and reconstructs the underlying request flow — including authentication, session management, and error recovery

3. The workflow becomes a single API endpoint that any system can call

Key differences

Factor	RPA	API-based automation
Speed	Operates at UI speed (seconds per action)	Operates at network speed (milliseconds per call)
Reliability	Breaks when UI changes	Independent of UI layer
Scalability	Limited concurrency	Standard API scaling
AI integration	Requires middleware	Direct endpoint call
Maintenance	30-50% of budget	Managed by the platform

When each approach makes sense

RPA is reasonable for:

Desktop applications with no web interface
Tasks where the automation volume is low
Organizations with existing RPA investment and established governance

API-based automation is better for:

Web-based software workflows
High-volume, production-grade automation
AI agent integrations that need programmatic access
Teams that want to build products on top of automated workflows, not just reduce manual work

The shift happening now

The rise of AI agents is accelerating the move from RPA to API-based automation. AI agents need to call endpoints, not click buttons. They need structured responses, not screen captures. They need millisecond latency, not multi-second UI interactions.

If your automation strategy is built on RPA and you're now integrating AI agents, you're likely building adapter layers between your bots and your agents. That's a sign the underlying approach needs to change.