Anu
Bilegdemberel

Problem

HANA already logs sessions, statements, and waits in detail. Operators still drowned in alerts, took too long to find root cause, and governance got shaky when people acted on a half-formed theory.

Solution

Python pipeline: SQL and SQLScript features feed hybrid rules with confidence-ranked root-cause hypotheses; impact is explicit; remediations only from an allowlist with human approval and rollback. Optional assistants may polish narrative text—they never decide safety or ranking.

Evaluation & metrics

• Offline eval: precision@k on root cause, false-alert checks, zero tolerance on allowlist violations, narrative rubric completeness
• scripts/eval_run.py writes baseline vs improved profiles (e.g. alert dedup) to reports/eval-baseline-vs-v2.md

Security & reliability

No open-ended auto-fix. High-risk steps need a human. Decisions and actions append to an audit log. Secrets stay in .env or BTP destinations, not in git.

Problem

Planning UIs regress in three places: pure totals drift, HTTP responses drift from the engine, and the browser shows a different number than the API. Without one canonical fixture set, teams ship silent total bugs.

Solution

Monorepo: pure TypeScript engine with decimal-safe aggregation; Fastify + Zod + OpenAPI for POST /v1/evaluate; React (Vite) grid with debounced recalc, AbortController, and X-Request-Id; Playwright @smoke asserts the same grandTotal strings as Vitest and supertest—CI runs unit → contract → E2E with Playwright artifacts on failure.

Evaluation & metrics

• Three fixtures drive identical string totals across engine, API, and DOM (no float coercion on money-shaped fields)
• docs/release-smoke.md: ~3-minute demo path; README interview script for walkthrough

Security & reliability

Allowlisted fixture ids; Zod validation on API surface; no auth in this slice—focus on correctness and request-id traceability for debugging narratives.