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kontextual-engine/docs/observability-export-enterprise-readiness.md

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# Observability Export And Enterprise Readiness
Date: 2026-05-06
Status: implemented MVP note for `KONT-WP-0010`.
## Purpose
This note records the operator-facing surfaces that make the engine
inspectable, recoverable, exportable, and measurable without direct storage
access. The implementation is intentionally an adapter layer over existing
runtime services, repository contracts, policy decisions, and audit events.
## Implemented Surfaces
- `GET /api/v1/operations/metrics`
- `GET /api/v1/operations/jobs`
- `GET /api/v1/operations/events`
- `GET /api/v1/operations/recovery/actions`
- `POST /api/v1/operations/recovery/{action}`
- `POST /api/v1/exports`
- `POST /api/v1/exports/validate`
- `POST /api/v1/governance/report`
- `GET /api/v1/extensions/catalog`
- `POST /api/v1/extensions/events`
- `POST /api/v1/quality/signals`
- `GET /api/v1/quality/cost`
- `GET /api/v1/performance/smoke`
- `GET /api/v1/compliance/mvp`
## Boundary Decisions
Operational metrics are computed from durable repository state and audit
events. API request latency is reported as an empty observation set until the
deployed FastAPI service adds middleware timing.
Recovery actions are explicit and policy checked. They dispatch through the
same runtime methods as normal service use: ingestion retry, transformation
retry/cancel, workflow retry/cancel, retrieval index refresh, and failure
inspection.
Export packages are governed envelopes, not raw database dumps. They include
assets, metadata, representations, relationships, versions, derived lineage,
audit references, adapter sections, manifest counts, a content hash, actor, and
policy context.
Governance reports avoid embedding source content. Findings identify missing
ownership, metadata, source references, audit gaps, and sensitive assets without
review or retention metadata.
Extension readiness is expressed through semantic event types, connector and
extractor capabilities, transformation operation metadata, backend abstraction
names, and explicit Markitect adapter boundaries.
Quality and cost signals are audit-backed observations. Retrieval quality uses
existing retrieval feedback metrics; AI cost and usage depend on adapters
providing token, provider, error, and estimated-cost fields.