posts
filtered by #observability clear
Observability Design for the AI Era — Reconciling PII Protection With AI Searchability, and Driving Self-Healing
Part 1 laid out four monitoring axes (application / infrastructure / CI / LLM) and the shape each one ends up in. Part 2 picks up where the data actually flows: it's production data, with PII in it. This post is about a multi-layer PII design that hashes at both write and search time with the same function, an integration surface where humans (web dashboard) and AI (MCP) share the same backend, and how all of that becomes the real driver of Self-Healing — running from CI failure to PR proposal end-to-end.
Observability Design for the AI Era — Application / Infrastructure / CI / LLM, Each in Its Own Shape
The previous code-graph series was about reshaping a static analysis graph so AI could query it. The same kind of reshaping is needed on the observability side. This post walks through four axes — application / infrastructure / CI / LLM — and the deliberately different shapes each one ends up in. The design judgments worth calling out: computing Gemini cost client-side instead of from billing API, sending Claude Code OTel straight to BigQuery instead of Loki, and shipping CI logs via post-hoc pull instead of webhook push.
Fixed Before Anyone Notices, Stronger After Every Fix: Self-Healing + Recurrence Prevention
Series Part 4. Production alerts trigger AI investigation, fix PR, auto-review, auto-merge, auto-redeploy. The same fix PR is required to add a new Guide -- a lint rule, CI guard, type constraint, or guideline entry -- so the same anti-pattern gets auto-rejected from then on. 115 Self-Healing PRs merged in the past 30 days, and the quality gates compound over time.