micro1

Overview

micro1 is a data lab focused on expert human data, training environments, and agent evaluation. Its public offering presents Realm for environments and frontier evaluations, Cortex for contextual evaluation of production agents, and a robotics-data program for embodied systems.

Realm is described as reproducing real-world scenarios for training and assessing agent actions. Cortex focuses on understanding and improving performance in context. The robotics offering supplies expert demonstrations and real-world data. Published research includes legal reasoning, financial analysis, and interpretation of pathology reports.

These offerings address different sources of training signal. Human expertise can establish what good work looks like, environments can support repeated attempts, and evaluation can identify where a deployed agent fails. The public product family does not mean that every customer receives all three, or that a result in one domain transfers automatically to another.

How the components can work together

Our assessment is that the combination is useful when a team wants to turn observed failures into targeted training work. In an illustrative office-agent project, evaluation might reveal that the system loses track of information across several documents. A training task could reproduce that dependency, and expert review could establish whether the final synthesis is defensible.

Physical demonstrations involve a different challenge: aligning observations and actions under the conditions a robot will face. The purchasing criteria for that data should therefore be distinct from those for text reasoning or enterprise workflows, even when the supplier is the same.

Evaluating fit

Identify the relevant product and the failure it is intended to address. For Realm, inspect task diversity, state control, and scoring. For Cortex, ask what evidence supports each judgment and how evaluation relates to the buyer's actual workflow. For robotics, examine collection conditions, action representations, and demonstration quality. A pilot should use separate tests and measure specific capability changes rather than only an overall score. Confirm contributor qualifications where professional expertise matters, and clarify rights to source data and derived examples. Delivery format, ongoing service responsibilities, and acceptance criteria should be agreed for each part of the engagement.