Build an Advanced Visual Web Agent with OpenAI Agents SDK and GPT-5 Pro
Develop a sophisticated multi-agent system designed to interact with web interfaces visually, mimicking human browser usage without relying on direct HTML parsing. This challenge leverages OpenAI Agents SDK for orchestrating agent teams, enabling them to collaboratively perform complex tasks. Agents, powered by the advanced reasoning capabilities of GPT-5 Pro, will execute high-level planning and decision-making. BrowserUse will be utilized to accelerate the development of custom browser automation tools (e.g., using Playwright or Selenium), allowing agents to perform precise visual interactions. Gentrace provides critical evaluation and observability pipelines to monitor and refine agent performance, while Sarvam AI enables intuitive voice-activated commands for controlling the agent system, making it highly accessible for real-world applications such as subscriber analysis on a simulated Beehiiv-like platform and SEO optimization.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a sophisticated multi-agent system designed to interact with web interfaces visually, mimicking human browser usage without relying on direct HTML parsing. This challenge leverages OpenAI Agents SDK for orchestrating agent teams, enabling them to collaboratively perform complex tasks. Agents, powered by the advanced reasoning capabilities of GPT-5 Pro, will execute high-level planning and decision-making. BrowserUse will be utilized to accelerate the development of custom browser automation tools (e.g., using Playwright or Selenium), allowing agents to perform precise visual interactions. Gentrace provides critical evaluation and observability pipelines to monitor and refine agent performance, while Sarvam AI enables intuitive voice-activated commands for controlling the agent system, making it highly accessible for real-world applications such as subscriber analysis on a simulated Beehiiv-like platform and SEO optimization.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
CorrectInformationExtraction
Verifies that the agent extracts correct numerical and textual information from the simulated web interface.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ToolExecutionSuccess
Checks if all required browser automation tools were successfully invoked and completed their actions without errors.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
VoiceCommandResponsiveness
Assesses if the agent correctly interprets and responds to specified voice commands.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
TaskCompletionRate
Percentage of tasks successfully completed end-to-end. • target: 90 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ExecutionTime
Average time taken to complete a task in seconds. • target: 60 • range: 0-300
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master OpenAI Agents SDK for building and managing autonomous agents with function calling, tool use, and multi-turn conversational capabilities
Design and implement robust visual web interaction tools using modern browser automation frameworks (e.g., Playwright) to enable agents to navigate and interact with web UIs without direct HTML parsing
Integrate Portia AI for defining agent roles, managing their configurations, and overseeing their lifecycle within a multi-agent system
Leverage BrowserUse for accelerated development and refinement of custom tool code, enhancing agent capabilities for specific web automation tasks
Establish comprehensive evaluation and observability pipelines using Gentrace to monitor agent decision-making, tool execution, and overall task completion accuracy
Implement voice-activated command processing using Sarvam AI to provide a natural language interface for directing and monitoring the visual web agent system
Build extended reasoning pipelines with GPT-5 Pro to enable advanced planning, problem-solving, and adaptive strategy formulation for unforeseen web scenarios
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsAI Research & Mentorship
Participation status
You haven't started this challenge yet
Operating window
Key dates and the organization behind this challenge.
Find another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.