Web Agent

AgentX Phase 2 Computer Use & Web Agent Track. Reflexive Agent Architecture for WebShop+ (budget management, preference memory, negative constraints, comparative reasoning, error recovery, cart management, checkout).

Our green-agent evaluates how well a white agent can understand and predict user shopping behavior in the context of online grocery shopping. The green-agent sets up a test in which white agents will be given both a user’s past purchases and the documentation for a shopping API, and white agents will have to use said shopping API to build the best basket for the shopper given the context. Ground truth will be measured against what the users ultimately purchased (as derived from the real transaction dataset). We built a green agent to test how well white agents can auto-shop for your groceries given previous purchases. We will provide an agent with a partial transaction history for a given user which contains their last n shopping trips and provide said agent with an e-commerce API (built in house on training data) so they can make searches, view results, and build a basket. When the agent is done building said users' n+1 basket, we check and see what % of items they predicted which the user actually checked out (since we have the users’ complete transaction history).

Green agent evaluates the browserGym benchmarks (currently miniwob++, assistbench and WebLINX)

Evaluates Q&A agents on their ability to answer questions about video content. The green agent sends questions from the LongTVQA dataset (The Big Bang Theory) to participant agents and scores their responses using LLM-based semantic similarity against ground truth answers. Scores range from 0.0 (completely incorrect) to 1.0 (semantically equivalent). Supports multiple judge models including Gemini, Claude etc