
AgentHub is an implementation-agnostic platform for evaluating AI agents in realistic simulation environment sandboxes. Use cases that we support include computer-use, browser, conversational, and tool-use agents. We help teams test, trace, track, and improve agent behavior at scale before deployment, closing the gap between vibe checks and real-world performance.
👉 Check us out AgentHubLabs.com
💬 If you’re building agents, training models, or can connect us to someone who is, we’d love to chat!
https://www.youtube.com/watch?v=1gPVYcphmmY
LLM-based agents often pass simple evals at a small scale and in specific cases but fail in deployment. They hallucinate tool use, misuse APIs, or follow an inefficient (or incorrect) reasoning trajectory that isn’t captured in standard benchmarks. There’s no standard way to evaluate agents beyond prompt-based tests - especially across tool use, UI interactions, and multi-step reasoning.
AgentHub lets you simulate real-world environments and evaluate agents end-to-end using structured tasks, traces, and grading:
Whether you’re testing autonomous workflows, tool-using copilots, or browser agents, we give you a sandboxed, realistic playground to evaluate in before you ship.
Youssef was previously a tech lead on the Foundation Model Evaluation team at Apple and studied CS at CMU.
Sandra has been an engineer at multiple startups, Google, and Meta, and studied CS and design at MIT.
We’ve been friends since a few years ago through a summer spent in Seattle and are obsessed with building the missing critical infrastructure to help teams evaluate, debug, and trust their agents.