Foundry

Foundry is a platform designed for building and evaluating browser agents. It allows users to configure tasks and define evaluation criteria, enabling the collection of high-quality data for reinforcement learning (RL) and agent improvement. The platform is particularly useful for developers and researchers who need to create reliable web agents that can perform tasks effectively in a controlled environment. By providing tools for task configuration and evaluation, Foundry helps users streamline the process of agent development and testing. The target audience for Foundry includes developers, data scientists, and researchers who are focused on creating and refining browser agents. These users require a robust platform that can handle the complexities of web interactions while ensuring that the data collected is accurate and reliable. Foundry meets these needs by offering a deterministic web simulator and an annotation framework, which are essential for gathering high-quality labels and benchmarking agent performance. One of the unique value propositions of Foundry is its ability to provide reproducible testing environments. This is crucial for users who want to ensure that their agents are evaluated fairly and consistently. The platform eliminates concerns about web drift, IP bans, or rate limits, which can often complicate the testing of web agents. By using Foundry, users can focus on improving their agents without the unpredictability associated with live web testing. Key differentiators of Foundry include its scalable annotation capabilities and its focus on continuous improvement. The platform allows users to collect ground truth labels efficiently, which is vital for training and refining agents. Additionally, the debugging tools provided by Foundry enable users to identify performance issues and make necessary adjustments, ensuring that their agents are always performing at their best. In terms of technical implementation, Foundry leverages advanced web simulation techniques to create a controlled environment for agent evaluation. This deterministic approach allows for consistent results across multiple tests, making it easier for users to track improvements and make data-driven decisions. Overall, Foundry is a powerful tool for anyone looking to build and evaluate browser agents effectively.

Key Features

• Deterministic Web Simulator: This feature allows users to test their agents in a controlled environment, ensuring consistent results without the unpredictability of the live web.

• Annotation Framework: Users can collect high-quality labels for their agents, which is essential for training and improving performance.

• Scalable Evaluation: Foundry supports scalable evaluation processes, making it easier to benchmark agents and track their performance over time.

• Debugging Tools: The platform provides tools for debugging agent performance, helping users identify and fix issues quickly.

• Continuous Improvement: Users can continuously improve their agents based on the data collected, ensuring they remain effective and reliable.

FAQ

What is Foundry?

Foundry is a platform for building and evaluating browser agents, allowing users to configure tasks and collect data for reinforcement learning.

Who can use Foundry?

Foundry is designed for developers, data scientists, and researchers who are focused on creating and refining browser agents.

How does Foundry help with agent evaluation?

Foundry provides a deterministic web simulator and an annotation framework, which help users collect high-quality labels and benchmark agent performance.

What are the benefits of using a deterministic web simulator?

The deterministic web simulator ensures consistent testing results, eliminating issues like web drift, IP bans, or rate limits.

Can I improve my agents using Foundry?

Yes, Foundry allows for continuous improvement of agents based on the data collected during evaluations.

What kind of data can I collect with Foundry?

Users can collect high-quality labels that are essential for training and improving the performance of their agents.