Platform Observability & Agent Insights

I designed and implemented repository-health scoring, the collection path, backend APIs, dashboard, and filters around existing telemetry. The work made those signals queryable and comparable while keeping the underlying telemetry as shared platform input.

I also connected a selected case to deeper investigation and follow-up questions. Jira creation remains a deliberate user click, so a low score does not quietly become a work item.

Existing telemetry enters the backend, which applies the repository-health scoring logic I designed and serves the score through the API. The dashboard and filters turn that score into a way to compare engineering repositories, inspect one, and narrow the condition that needs attention.

If the engineer opens a particular case or asks a question, the selected repository context moves into the deeper investigation path. Follow-up work is then an explicit action after that inspection, not a side effect of routine collection.

Keeping routine scoring deterministic gives the fleet a stable baseline. I reserved the agent for a named case or question, rather than spend compute and attention generating a new explanation for every ordinary score.

That choice puts the first investigative choice with the engineer. Filters preserve the comparison context, while the deeper path can focus on the repository and condition that actually prompted the question.

When a selected case has missing or conflicting inputs, the dashboard keeps the case and its available context visible. The score stays traceable to the same collection path instead of being silently replaced with a speculative explanation.

The engineer can refine the filters, ask a focused question, or capture follow-up work after reviewing the case. That keeps an observable gap separate from the decision to act on it.