Search Results for "loop root"

...Human-in-the-loop interaction is central: developers can guide reasoning by selecting relevant spans or logs, then verify agent reasoning through traceable context.

...The CLI is designed as a verification layer for agentic coding workflows, so an AI agent can create tests, run them, inspect failures, fix code, and rerun checks. It returns structured failure bundles that can include failing steps, screenshots, DOM snapshots, test source, root-cause hypotheses, and recommended fix targets. It supports scripting, deterministic output, JSON results, predictable exit codes, and dry-run behavior for automation. Overall, it helps teams turn AI-generated code into a more testable, repeatable, and evidence-backed development loop.

NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data...