What you should have at the agent evaluation checkpoint
By the time you reach the evaluation checkpoint in the agent-building process, you should have three things locked in: an evaluated agent with known failure modes, measured costs, and a golden dataset. Knowing your failure modes is just as important as knowing what works — it tells you where the agent breaks down and gives you a clear path to improvement.
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MembersRubrics: how Hyper Agent scores agent output quality automatically
Hyper Agent has a feature called rubrics — essentially an eval rubric that lets you define what 'good' looks like for a specific type of task. For example, you could create a rubric called 'What makes great Greg Isenberg content?' and pin i
Small businesses are deploying AI agents faster than 50,000-person companies ever could
In Hyper Agent's early adoption base, small shops have become surprisingly sophisticated very quickly and are running their operations in genuinely game-changing ways. The reason is simple: a 50,000-person company can't pivot on a dime — th
Hyper Agent targets both power users and casual users with the same product
The design philosophy behind Hyper Agent is to serve the full range of users — from power users who want granular control over how the agent is set up, so they can squeeze out maximum bleeding-edge performance, all the way to people who jus