Comet

The platform provides a unified interface for logging, visualizing, and comparing machine‑learning experiment runs. It captures detailed trace data from model training and inference, allowing users to examine each step of an agent’s execution, from context retrieval to tool selection and feedback scoring. Built‑in test suites let developers define assertions in plain English, automatically evaluating traces and reporting pass/fail outcomes.

In addition to observability, the system offers automated remediation through a coding agent that analyzes test results, suggests fixes, and writes code changes back to the repository with version‑control integration. A sandboxed playground enables safe experimentation with modified agents, supporting stakeholder review and deployment of selected versions.

The service also extends to production monitoring, tracking model costs, governance metrics, and performance consistency at scale. It is positioned for data scientists, machine‑learning engineers, and AI developers who need to track, debug, and iterate on model training and deployment pipelines.