Benjamin Admin
98f67e75d9
Turn the architecture inside-out: instead of refining classes/registries/journeys, force the whole
platform to behave as ONE expert system and run a real consulting project end-to-end — measuring how
often the consultant has to "jump" (special-case glue instead of a clean engine-to-engine handoff). A
Reference Scenario asks "is the knowledge correct?"; a Customer Mission asks "can a customer WORK with
it?". This is the last big architecture test before broad corpus expansion.
- reference_scenarios/mission_machine_builder.py: a synthetic machine builder (ISO9001 + ISMS + CE +
PLC + remote maintenance + cloud + 80 devs + EU; no real names) asks "what must I do in the next 6
months?". Runs the REAL engines: Regulatory Map -> Journey selection -> Capability Delta (RS-005) ->
Roadmap (leverage) -> Playbooks -> Evidence -> Verification -> Completeness, and produces the 6-month
consulting answer ("the top-5 measures close 9/16 = 56%, starting with the ones that satisfy CRA AND
MaschinenVO at once").
- Flow-Continuity audit (the actual test): 5 CLEAN, 2 JUMPS, 2 deliberate DEPENDENCIES. The two real
seams: (1) Scope -> Journey (no `certs x targets -> journeys` selector engine; the data exists in
transitions.yaml, only the selection is glue); (2) Evidence -> Verification (parked, Vision V2). The
two dependencies (cert->capability map @Execution, corpus_status curation) are intended ownership
boundaries, not architecture breaks.
- Finding: the platform carries the WHOLE consulting flow end-to-end. Once the Scope->Journey selector
exists, the foundation is essentially done — from there the work is knowledge, not architecture.
4 end-to-end tests (mission runs, exactly two known jumps, full flow present, no real company names).
check-loc 0. Non-runtime harness -> no deploy (ADR-001).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
backend-compliance
Python/FastAPI service implementing the DSGVO compliance API: DSR, DSFA, consent, controls, risks, evidence, audit, vendor management, ISMS, change requests, document generation.
Port: 8002 (container: bp-compliance-backend)
Stack: Python 3.12, FastAPI, SQLAlchemy 2.x, Alembic, Keycloak auth.
Architecture
compliance/
├── api/ # Routers (thin, ≤30 LOC per handler)
├── services/ # Business logic
├── repositories/ # DB access
├── domain/ # Value objects, domain errors
├── schemas/ # Pydantic models, split per domain
└── db/models/ # SQLAlchemy ORM, one module per aggregate
The service follows this layered target structure but not all files are fully refactored yet. Phase 1 backlog is tracked in .claude/rules/loc-exceptions.txt (27 backend-compliance files currently excepted).
See ../AGENTS.python.md for the full convention and ../.claude/rules/architecture.md for the non-negotiable rules.
Run locally
cd backend-compliance
pip install -r requirements.txt
export COMPLIANCE_DATABASE_URL=... # Postgres (Hetzner or local)
uvicorn main:app --reload --port 8002
Tests
pytest compliance/tests/ -v
pytest --cov=compliance --cov-report=term-missing
Layout: tests/unit/, tests/integration/, tests/contracts/. Contract tests diff /openapi.json against tests/contracts/openapi.baseline.json.
Public API surface
404+ endpoints across /api/v1/*. Grouped by domain: ai, audit, consent, dsfa, dsr, gdpr, vendor, evidence, change-requests, generation, projects, company-profile, isms. Every path is a contract — see the "Public endpoints" rule in the root CLAUDE.md.
Environment
| Var | Purpose |
|---|---|
COMPLIANCE_DATABASE_URL |
Postgres DSN, sslmode=require |
KEYCLOAK_* |
Auth verification |
QDRANT_URL, QDRANT_API_KEY |
Vector search |
CORE_VALKEY_URL |
Session cache |
Don't touch
Database schema, __tablename__, column names, existing migrations under migrations/. See root CLAUDE.md rule 3.