Web Agent

green agent evaluates WebShop shopping tasks in a text‑only Gym environment. It orchestrates episodes by resetting the environment, sending observations to the purple agent, executing returned actions (search/click/buy), and collecting programmatic rewards. It reports structured JSON artifacts containing total reward, success, and per‑step traces. This provides a reproducible benchmark for instruction following in e‑commerce search and product selection without an LLM judge.

I present WebJudge Green Agent, a vision-based evaluator for generalist web navigation agents based on the Online-Mind2Web benchmark. Unlike traditional DOM-based evaluators that break with UI updates, our system utilizes a neuro-symbolic 3-step pipeline (Key Point Extraction, Visual Filtering, Verdict Generation) powered by GPT-4o Vision to evaluate agent trajectories on live websites. The project features: A fully Dockerized environment compliant with the AgentBeats A2A protocol. A dynamic task generation system with a diverse dataset (Shopping, Travel, Finance). An intelligent judging engine capable of analyzing screenshots to verify task completion strictly and fairly.

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)