Search⌘ K
AI Features
Home
Courses
Agentic System Design Crash Course

Designing Multimodal Web Agents

Explore how to design multimodal web agents that autonomously navigate and interact with dynamic websites. Understand the architecture combining visual perception and structured actions to address challenges in grounding and control. Learn iterative control patterns, context management, and evaluation methods to build robust agentic systems for real-world web tasks.

We'll cover the following...

In this lesson, we explore how to design a multimodal web navigation agent, a system that autonomously interacts with real websites by observing, reasoning, and taking actions like a human user. Because the web is dynamic, visually structured, and constantly changing, building such agents introduces unique challenges in perception, grounding, and control. Through a structured design analysis, we examine how these challenges are addressed architecturally and what we can learn for designing robust agentic systems more broadly.

A web agent must be able to perform actions like a human, such as clicking buttons, to complete its tasks
A web agent must be able to perform actions like a human, such as clicking buttons, to complete its tasks

The multimodal web agent challenge

A web agent is an autonomous system that navigates and interacts with real-world websites to complete tasks on behalf of a user. Unlike a simple web scraper, it performs actions such as clicking buttons, filling out forms, selecting options, and navigating multiple pages. The challenge is that modern websites are:

  • Dynamic and constantly changing

  • Visually structured rather than purely textual

  • Built on large, noisy HTML codebases

  • Designed for human perception, not machine parsing

Early web agents relied entirely on HTML to understand pages. While HTML allows an agent to identify elements and extract text, it has two critical limitations:

  • It is verbose and difficult for language models to interpret reliably.

  • It lacks visual context, making the agent effectively “blind” to layout and spatial relationships.

A text-only agent is unaware, and perceiving the web as a complex wall of HTML code while a multimodal agent can see the rendered webpage, giving it a richer, more human-like understanding of the environment
A text-only agent is unaware, and perceiving the web as a complex wall of HTML code while a multimodal agent can see the rendered webpage, giving it a richer, more human-like understanding of the environment

This limitation led to the rise of multimodal web agents that process screenshots of rendered pages alongside textual signals. However, visual perception introduces a new bottleneck: the grounding problem. Every web action requires two distinct steps:

  1. Action generation: Deciding what should be done (e.g., “click the Search button”).

  2. Action grounding: Identifying exactly which on-screen element corresponds to that instruction.

Modern multimodal models are often strong at action generation. The primary failure point lies in grounding, mapping a correct decision to the correct interactive element in a crowded interface. Designing a capable web agent, therefore, requires solving not only reasoning, but perception–action alignment. From this ...