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New Deterministic Agentic Workflow with Applitools Visual AI MCP Server

Coding agents write code fast, but they struggle to see their own work. They waste your tokens by staring at messy screenshots with general-purpose vision LLMs or guessing over and over again just to force a passing test. They aren’t bad coders; they just need real eyes. 

Enter the new Applitools Eyes MCP Server.

Join us for this session to see how Applitools Eyes MCP connects deterministic Visual AI right into your agent’s chat or terminal so that your AI can actually see what it builds.

In this live session, you’ll see how to:

  • Get a clear pass or fail signal: Help your agents spot real visual bugs instantly without wasting money on extra AI tokens.
  • Connect live code straight to Figma: Use Figma links as your target picture to automatically catch design mistakes and sizing bugs.
  • Trade messy code for simple English: Ditch easily-broken code rules for plain-English steps in your test code that keep working even when your code changes.
  • Expedite agentic development: Cut human review cycles and complexity so your agents ship faster.

Join us live to see how to give your AI agents real visual sight!

Coding agents write code fast, but they struggle to see their own work. They waste your tokens by staring at messy screenshots with general-purpose vision LLMs or guessing over and over again just to force a passing test. They aren't bad coders; they just need real eyes. 

Enter the new Applitools Eyes MCP Server.

Join us for this session to see how Applitools Eyes MCP connects deterministic Visual AI right into your agent's chat or terminal so that your AI can actually see what it builds.

In this session, you’ll see how to:

  • Get a clear pass or fail signal: Help your agents spot real visual bugs instantly without wasting money on extra AI tokens.
  • Connect live code straight to Figma: Use Figma links as your target picture to automatically catch design mistakes and sizing bugs.
  • Trade messy code for simple English: Ditch easily-broken code rules for plain-English steps in your test code that keep working even when your code changes.
  • Expedite agentic development: Cut human review cycles and complexity so your agents ship faster.

Join us to see how to give your AI agents real visual sight!

New Deterministic Agentic Workflow with Applitools Visual AI MCP Server

Applitools is the world’s most intelligent test automation platform. See it in action with a personalized demo.

Expert Speaker

Adam Carmi
Adam Carmi

Applitools Co-Founder & CTO

Adam Carmi is the Co-Founder and CTO of Applitools, the creator of Visual Testing and a leader in applying AI to test automation. With over two decades of leadership in software engineering and innovation, Adam has driven the development of AI powered solutions used by leading enterprises worldwide, and regularly shares insights on the evolving role of AI in test automation at conferences and industry events.

Why shouldn't developers use vision LLMs for UI testing?

Vision LLMs are probabilistic models, which leads to visual hallucinations and missed micro-pixel regressions when checking AI-generated code. This creates a "Probabilistic Validation Gap" while incurring high token costs. Deterministic Visual AI is required for reliable, pixel-accurate UI governance without visual guesswork.

How do Applitools Eyes MCP Tools work with AI coding agents?

Applitools Eyes MCP Tools integrate directly into the workflows of AI coding agents like Claude Code, Cursor, Copilot, and Cline. This allows agents to autonomously inspect exact pixel diffs, correlate layout changes to source code lines, and manage baseline resolutions directly within the developer's chat interface.

How can teams automatically enforce Figma designs against live code?

Engineering teams can link their Figma design frames directly to live running code using SDK native integration. This automatically sizes viewports to match design specifications, eliminating false positives caused by zoom mismatches and ensuring AI-generated code adheres perfectly to the intended design baselines.

What is intent-based test automation for AI workflows?

Intent-based test automation replaces fragile CSS and XPath selectors with plain-English commands, such as "Fill in email address." Powered by a secure, proprietary NLP engine, this approach creates resilient, self-healing tests that do not break when AI coding agents modify the underlying DOM structure.

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