Lobasters: Enterprise Agent Proving Ground

Lobasters is a local-first, open-source laboratory designed to move AI beyond simple copilots into the realm of fully autonomous, decision-making systems. Built for the "AI Agent Olympics," Lobasters provides the infrastructure to build, red-team, and validate agents in high-stakes enterprise environments without compromising data privacy. The Three Pillars of Lobasters 1. The Arena (Adversarial Benchmarking) The Arena is a structured adversarial environment where two models engage in multi-turn debate. Unlike traditional chat interfaces, the Arena captures hidden reasoning chains and allows agents to use custom semantic tools. This enables developers to observe how agents handle roadblocks and adjust their strategies in real-time when faced with a sophisticated opponent. 2. The Proving Ground (Automated Examination) To ensure enterprise utility, the Proving Ground uses a hierarchical "Teacher-Student" flow. A high-reasoning Teacher model quizzes a candidate Student agent across specific knowledge domains. The system automatically grades performance using customizable scales (S/A/B/F), providing a rigorous, automated audit of an agent’s specialized capabilities before they are deployed to production. 3. LAB: LM-Zero (Autonomous Sandbox) LAB is a virtual filesystem environment where agents operate with true autonomy. A Master Agent receives complex objectives and must plan, execute, and self-correct using a toolkit of helper agents and a persistent scratchpad. It transforms long-running, multi-step agentic workflows from experimental concepts into observable, reproducible engineering tasks. Enterprise-Ready & Privacy-First Lobasters is built with Next.js 16 and a strictly local-first architecture. All API keys and session data remain in the user's browser, making it the ideal choice for researchers and managers at Milan AI Week who require state-of-the-art agent evaluation without the security risks of third-party cloud storage.