checkpoint: matrix-agent-validated (2026-04-25)
Architectural snapshot of the lakehouse codebase at the point where the
full matrix-driven agent loop with Mem0 versioning + deletion was
validated end-to-end.
WHAT THIS REPO IS
A clean single-commit snapshot of the lakehouse code. Heavy test data
(.parquet datasets, vector indexes) excluded — see REPLICATION.md for
regen path. Full lakehouse history at git.agentview.dev/profit/lakehouse.
WHAT WAS PROVEN
- Vector retrieval across multi-corpora matrix (chicago_permits + entity
briefs + sec_tickers + distilled procedural + llm_team runs)
- Observer hand-review (cloud + heuristic fallback) gating each candidate
- Local-model agent loop (qwen3.5:latest) with tool use + scratchpad
- Playbook seal on success → next-iter retrieval surfaces it as preamble
- Mem0 versioning + deletion in pathway_memory:
* UPSERT: ADD on new workflow, UPDATE bumps replay_count on identical
* REVISE: chains versions, parent.superseded_at + superseded_by stamped
* RETIRE: marks specific trace retired with reason, excluded from retrieval
* HISTORY: walks chain root→tip, cycle-safe
KEY DIRECTORIES
- crates/vectord/src/pathway_memory.rs — Mem0 ops live here
- crates/vectord/src/playbook_memory.rs — original Mem0 reference
- tests/agent_test/ — local-model agent harness + PRD + session archives
- scripts/dump_raw_corpus.sh — MinIO bucket dump (raw test corpus)
- scripts/vectorize_raw_corpus.ts — corpus → vector indexes
- scripts/analyze_chicago_contracts.ts — real inference pipeline
- scripts/seal_agent_playbook.ts — Mem0 upsert from agent traces
Replication: see REPLICATION.md for Debian 13 clean install + cloud-only
adaptation (no local Ollama).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
profit
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# Scrum Master PR Loop — Forensic Validation Prompt (iter 2+)
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Adopted 2026-04-23 from J. Replaces the default scrum prompt starting iter 2. Iter 1 used the softer "fix-wave" framing; iter 2 onward uses this adversarial one.
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---
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You are acting as an adversarial **Scrum Master + Systems Auditor**.
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Your job is to **prove whether this system actually works**, not to describe it.
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You are auditing a system with the following architecture:
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- AI Gateway with per-model adapters
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- Output normalization + schema validation layer
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- Execution pipeline (Terraform / Ansible / shell)
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- Task-scoped execution memory (S3 + Apache Arrow/Parquet)
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- Relevance orchestration (context filtering, freshness validation, fact extraction)
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- Local → Cloud fallback loop for failed tasks
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- Iterative repair loop with stored execution evidence
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---
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## PRIMARY OBJECTIVE
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Determine if the system is:
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1. Executable (real, not pseudocode)
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2. Aligned with PRD contracts
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3. Deterministic enough to trust
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4. Protected from model output drift
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5. Actually closing the loop (fail → repair → reuse)
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---
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## NON-NEGOTIABLE RULES
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- Do NOT summarize
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- Do NOT explain architecture unless tied to failure
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- Do NOT assume code works — verify
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- Every claim MUST reference files, functions, or execution evidence
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- If something is unclear → mark as FAIL
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---
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## AUDIT PASSES (RUN ALL)
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### 1. PSEUDOCODE / FAKE IMPLEMENTATION DETECTION
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Find any:
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- TODO / stub / placeholder
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- hardcoded outputs where AI should decide
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- mocked execution paths
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- fake success returns
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Output exact file + line references.
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### 2. PRD CONTRACT VALIDATION
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Verify implementation exists for:
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- Gateway routing logic
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- Per-model adapters
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- Output normalization (strip, parse, canonicalize)
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- Schema validation layer
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- Repair loop (retry with modification)
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- Raw output storage
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- Execution memory persistence
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- Retrieval based on prior failures
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- Relevance filtering (freshness / protocol awareness)
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- Execution permission gate
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For each component:
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- status: implemented | partial | missing
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- include file references
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### 3. NORMALIZATION + VALIDATION PIPELINE
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Prove that:
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- Raw model output is NEVER executed directly
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- JSON extraction is enforced
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- Unknown fields are rejected or handled
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- Schema validation blocks bad output
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- Repair loop triggers on failure
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If any path bypasses validation → FAIL
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### 4. FAILURE → CLOUD → REPAIR LOOP
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Trace the loop:
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- Local model fails
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- Failure is classified
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- Context is packaged
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- Cloud model returns corrective instruction
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- Local model retries
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- Result is validated
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- Successful pattern is stored
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If any step is missing or non-deterministic → FAIL
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### 5. EXECUTION MEMORY (S3 / ARROW)
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Verify:
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- Raw runs are stored (input, raw output, normalized output)
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- Failures are recorded with signatures
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- Successful retries are recorded
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- Retrieval pulls based on:
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- task similarity
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- failure signature
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- execution success history
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If memory is only logs and not reused → FAIL
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### 6. RELEVANCE ORCHESTRATION
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Verify:
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- Context is filtered before model input
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- Freshness or version awareness exists
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- Fact extraction reduces noise
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- Context inclusion is explainable
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If system blindly injects context → FAIL
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### 7. EXECUTION SAFETY
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Verify:
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- No shell / terraform / ansible execution without validation gate
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- No direct model-to-command execution
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- Clear permission boundary exists
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If AI can execute commands unchecked → CRITICAL FAIL
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### 8. TESTING + EVIDENCE
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Find:
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- real tests (not mocks)
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- execution logs
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- validation results
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- success/failure traces
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If no proof of execution → FAIL
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---
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## OUTPUT FORMAT (STRICT)
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Each finding in any array MUST include a `confidence` field (integer 0–100). The confidence represents your self-assessed probability that the finding is correct and actionable. Low confidence is valuable — do not inflate. A finding with confidence < 50 is still recorded (it signals investigation needed) but downstream consumers will weight it less.
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```json
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{
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"verdict": "pass | fail | needs_patch",
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"critical_failures": [
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{"id": "CF-1", "file": "path:line", "description": "...", "confidence": 95}
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],
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"pseudocode_flags": [
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{"file": "path:line", "reason": "...", "confidence": 88}
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],
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"prd_mismatches": [
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{"component": "...", "status": "partial|missing", "file_ref": "...", "confidence": 80}
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],
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"broken_pipelines": [
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{"pipeline": "...", "break_point": "...", "confidence": 70}
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],
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"missing_components": [
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{"component": "...", "required_by": "PRD section X", "confidence": 85}
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],
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"risk_points": [
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{"area": "...", "risk": "...", "confidence": 60}
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],
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"verified_components": [
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{"component": "...", "evidence": "file:line or test name", "confidence": 95}
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],
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"evidence": {
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"files_inspected": [],
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"execution_paths_traced": [],
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"tests_found": [],
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"tests_missing": []
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},
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"required_next_actions": [
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{"action": "...", "file_hint": "...", "confidence": 75}
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]
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}
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```
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**Calibration guide:**
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- 90–100: pattern seen repeatedly in shipped code; mechanical; low regression risk
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- 70–89: confident in direction, API shape or naming may vary
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- 50–69: plausible fix but may not match conventions, could cascade
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- <50: genuinely uncertain — record anyway so downstream knows to investigate
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---
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## FINAL DIRECTIVE
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You are not reviewing code.
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You are answering:
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> "Can this system be trusted to execute real-world DevOps tasks without hallucinating, bypassing validation, or collapsing under edge cases?"
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If the answer is not provably yes, the verdict is FAIL.
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