HarnessML Agent-Computer Interface

Example Use Cases

• 1

  Run structured, reproducible machine learning experiments without writing training loops.

• 2

  Manage and log ML experiment lifecycles with enforced hypotheses and conclusions.

• 3

  Real-time observability and diagnostics of ML workflows via a companion dashboard.

Description

HarnessML provides an Agent-Computer Interface (ACI) for machine learning that replaces coding training loops with deterministic MCP tool calls. It supports data ingestion, feature engineering, model training, evaluation, and experiment management with enforced workflows and logging. This approach reduces boilerplate, enforces experiment discipline, and enables reproducible, logged ML experiments that survive session boundaries. A companion dashboard offers real-time observability and diagnostics.