posts
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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.
Making the Context Across 46 Repositories Semantically Searchable for AI
The biggest issue Part 1 left open was that AI couldn't reach the 46-repo codebase by natural-language query (the entry-point problem). This post is how I solved it — by reusing the pattern proven in db-graph (1,133-table semantic search), then layering minimal annotations only around boundary nodes. Covers the separate-branch operation that keeps engineers' daily workflow untouched, the SLO that protects the joins between three graphs, the SAME_ENTITY normalization, and the April–May trial-and-error timeline traced through real commits.
Building One Knowledge Graph Across 46 Repositories With Static Analysis
A static-analysis approach to unifying 46 repositories (37 air-closet-side + 9 mall-side) of legacy production code into one knowledge graph. Why simply 'letting AI read the code' isn't enough, why I had to chase down boundary nodes (API endpoints, DB tables, Event topics), how I dealt with framework and library diversity, and what 3 months of trial and error solved or didn't solve — looking back through actual git history.
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.
The Heart of the AI Harness: A Knowledge Graph of the AI, by the AI, for the AI
Series Part 2: how we built cortex-product-graph (cpg) — a unified knowledge graph of code, docs, DB schemas, and infrastructure for the cortex AI platform. Build pipeline with JSDoc/Pulumi/docs as SSoT, plus the Runbook tool-design pattern that guides AI through the graph.
Bridging 'I Want to Build' and 'I Want to Publish Safely' for Non-Engineers — Sandbox MCP
Non-engineers can build AI apps, but publishing safely is still gated by engineers. Sandbox MCP gives them a one-command path to deploy Web/API/DB/Cron with guardrails.
Still Measuring Initiative Impact Manually? How We Used Graph RAG + MCP to Make It Explorable
Measuring 'did that initiative actually work?' usually means manual SQL spelunking. We modeled initiatives × KPIs as a graph and let an LLM traverse it via MCP.
How We Built an Automated Meeting Intelligence System with Google Meet, Slack, and RAG
AI summaries aren't enough — context dies when a meeting ends. We pipe Google Meet recordings to Slack, transcribe everything, and make history queryable in natural language.
We Built 17 MCP Servers to Let AI Run Our Internal Operations
Overview of 17 MCP servers we built in three months at airCloset, covering DBs, infra, docs, project management, observability, CI/CD, and even non-engineer code edits.