Systems and methods are provided for improved generation and selection of robot sensor data for manual annotation and/or use in training machine learning models used to operate robots. An on-robot controller can operate to determine a cross-modal inconsistency, that a temporally proximate target task was failed, and/or that a confidence in a model output indicate that particular sensor data should be transmitted to a remote system for human annotation and/or use in updating the machine learning model(s) of the robot. Embedding vector(s) representing such selected sensor data (e.g., representing common aspects across a population of sets of sensor data) could also be determined and transmitted to the robot. The robot could then determine embeddings for sensor data and, if the embeddings are similar enough to the transmitted embedding(s), the sensor data could be transmitted to the remote system for annotation and/or model updating.

Access control and governance for automations is disclosed. A code analyzer of an automation designer application, such as a workflow analyzer, may read access control and governance policy rules for an automation designer application and analyze activities of a workflow, tools, and/or prompts of the automation designer application against the access control and governance policy rules. When one or more analyzed activities of the workflow, one or more tools, and/or one or more prompts violate the access control and governance policy rules, the code analyzer prevents generation of an automation robot or publication of the automation until the workflow, the tools, and/or the prompts satisfy the access control and governance policy rules. When the analyzed activities of the workflow, the tools, and or the prompts comply with all required access control and governance policy rules, the automation designer application may generate an automation or publish the automation.