deepsigma 2.0.4

Σ OVERWATCH — Institutional Decision Infrastructure for coherence, credibility, and drift governance

Σ OVERWATCH

Current pilot release: v2.0.4
See: docs/release/RELEASE_NOTES_v2.0.4.md

Repo Radar KPI (latest)

• Current release radar: release_kpis/radar_v2.0.4.png
• Composite release radar: release_kpis/radar_composite_latest.png
• Composite release delta table: release_kpis/radar_composite_latest.md
• Gate report: release_kpis/KPI_GATE_REPORT.md
• Issue label gate: release_kpis/ISSUE_LABEL_GATE_REPORT.md
• KPI history: release_kpis/history.json

Institutional Decision Infrastructure

Trust layer for agentic AI: verify before act, seal what happened, detect drift, ship patches.

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What It Does

Organizations make thousands of decisions. Almost none are structurally recorded with their reasoning, evidence, or assumptions. When leaders leave, conditions change, or AI accelerates decisions 100x — governance fails silently.

Σ OVERWATCH fills this gap with three primitives:

• Truth — Decision Ledger Records (DLR) capture what was decided, by whom, with what evidence
• Reasoning — Reasoning Scaffolds (RS) capture why — claims, counter-claims, weights
• Memory — Decision Scaffolds + Memory Graphs (DS + MG) make institutional knowledge queryable

When assumptions decay, Drift fires. When drift exceeds tolerance, a Patch corrects it. This is the Drift → Patch loop — continuous self-correction.

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Quick Start

pip install deepsigma

# One-command Money Demo (recommended first run)
make demo

# Health check
deepsigma doctor

# Score coherence (0–100, A–F)
python -m coherence_ops score ./coherence_ops/examples/sample_episodes.json --json

# Drift → Patch canonical entrypoint
python -m coherence_ops.examples.drift_patch_cycle

# Full 7-step Golden Path (no credentials needed)
deepsigma golden-path sharepoint \
  --fixture src/demos/golden_path/fixtures/sharepoint_small --clean

# Retention lifecycle sweep (cron-friendly)
deepsigma retention sweep --tenant tenant-alpha

Golden-Path Proof Artifacts

# Golden Path run (ingest -> drift -> patch -> recall)
PYTHONPATH=src python -m tools.golden_path_cli golden-path sharepoint \
  --fixture src/demos/golden_path/fixtures/sharepoint_small \
  --output golden_path_output --clean

# Trust Scorecard (includes WHY retrieval SLO check)
PYTHONPATH=src python -m tools.trust_scorecard \
  --input golden_path_output \
  --output golden_path_output/trust_scorecard.json

============================================================
  GOLDEN PATH
============================================================
  [1] CONNECT              PASS
  [2] NORMALIZE            PASS
  [3] EXTRACT              PASS
  [4] SEAL                 PASS
  [5] DRIFT                PASS
  [6] PATCH                PASS
  [7] RECALL               PASS
...
  IRIS:       WHY=RESOLVED, WHAT_CHANGED=RESOLVED, STATUS=RESOLVED
  Drift:      6 events
  Patch:      applied
============================================================
Trust Scorecard written to golden_path_output/trust_scorecard.json
  SLOs:    ALL PASS

Trust Scorecard highlights from the same run:

• iris_why_latency_ms: 1.4 (<= 60000 target, retrieval <= 60s)
• patch_applied: true
• drift_events_detected: 6
• all_steps_passed: true

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Court-Grade Proof (60 seconds)

# Seal + sign + authority bind + transparency log + pack
python src/tools/reconstruct/seal_and_prove.py \
    --decision-id DEC-001 --clock 2026-02-21T00:00:00Z \
    --sign-algo hmac --sign-key-id ds-dev --sign-key "$KEY" \
    --auto-authority --pack-dir /tmp/pack

# Verify everything in one command:
python src/tools/reconstruct/verify_pack.py --pack /tmp/pack --key "$KEY"

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Key Capabilities

Capability	Description	Docs
Coherence Ops CLI	Score, audit, query, reconcile decision artifacts	CLI Reference
Golden Path	7-step end-to-end proof loop	Golden Path
Credibility Engine	Institutional-scale claim lattice with formal scoring	Engine Docs
Trust Scorecard	Measurable SLOs from every Golden Path run	Spec
Excel-first BOOT	Govern decisions in a shared workbook — no code required	BOOT Protocol
MDPT	Multi-Dimensional Prompt Toolkit for governed prompt ops	MDPT Docs
Court-Grade Admissibility	Seal-and-prove pipeline: Merkle commitments, transparency log, multi-sig witness, hardware key hooks	Admissibility Levels
MCP Server	Model Context Protocol server with auth + rate limiting	MCP Adapter
API Reference	Auto-generated OpenAPI + static Redoc docs	API Docs
RDF/SPARQL	Semantic lattice queries via in-process SPARQL 1.1	SPARQL Service
Dashboard	React dashboard with Trust Scorecard + Zustand store	Dashboard

Connectors

All connectors conform to the Connector Contract v1.0.

Connector	Transport	Docs
SharePoint	Graph API	docs
Power Platform	Dataverse Web API	docs
AskSage	REST API	docs
Snowflake	Cortex + SQL API	docs
LangGraph	LangChain Callback	docs
OpenClaw	WASM Sandbox	docs
Local LLM	llama.cpp / OpenAI-compatible	docs

Security And Data Handling

• Data Boundaries — data at rest, storage locations, retention, redaction, tenancy isolation, connector flow, secrets policy, and network boundaries.
• Connector SDK — ConnectorV1 contract and safety expectations for custom adapters.

Monitoring

• Prometheus metrics endpoint: GET /metrics
• Grafana dashboard: ops/grafana/deepsigma.json
• Local monitoring stack: docker-compose.monitoring.yml

Start monitoring stack:

docker compose -f docker-compose.monitoring.yml up --build

Endpoints:

• DeepSigma API: http://localhost:8000
• Prometheus: http://localhost:9090
• Grafana: http://localhost:3001 (admin / admin)

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Repo Structure

DeepSigma/
├── src/                 # 12 Python packages (all source code)
│   ├── coherence_ops/   #   Core library + CLI
│   ├── engine/          #   Compression, degrade ladder, supervisor
│   ├── adapters/        #   MCP, SharePoint, Snowflake, LangGraph, OpenClaw, AskSage
│   ├── deepsigma/       #   Unified product CLI
│   ├── demos/           #   Golden Path, Excel-first Money Demo
│   ├── mdpt/            #   MDPT tools + Power App starter kit
│   └── ...              #   credibility_engine, services, mesh, governance, tenancy, verifiers, tools
├── tests/               # 1250+ tests, fixtures, datasets
├── docs/                # Documentation + examples (canonical, mermaid, lattices, etc.)
├── dashboard/           # React dashboard + API server
├── schemas/             # JSON schemas (core engine + Prompt OS)
├── artifacts/           # Workbooks, templates, sealed runs, sample data
├── prompts/             # Canonical prompts + Prompt OS control prompts
└── .github/             # CI/CD workflows

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Documentation

QUICKSTART.md	5-minute starter path (deepsigma init + make demo)
START_HERE.md	Front door
HERO_DEMO.md	5-minute hands-on walkthrough
NAV.md	Full navigation index
ABOUT.md	Reality Await Layer (RAL)
OPS_RUNBOOK.md	Operations + incident playbooks
HELM.md	Helm install/test runbook (kind/minikube)
STATELESS_API_SCALE_GUIDE.md	3-replica benchmark + sizing guidance
STABILITY.md	Versioning policy + stability guarantees
docs/99-docs-map.md	Complete docs map

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Excel Prompt OS v2

Structured cognition workbook for institutional decision-making — no code required.

• Workbook: artifacts/excel/Coherence_Prompt_OS_v2.xlsx
• Quickstart: docs/prompt_os/README.md
• Prompts: docs/prompt_os/PROMPTS.md
• Diagram: docs/prompt_os/diagrams/prompt_os_flow.mmd

Prompts

• Canonical Prompts v1: prompts/canonical/ — Executive Analysis, Reality Assessment
• Prompt Index: prompts/README.md

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Contributing

See CONTRIBUTING.md.

License

MIT

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Σ OVERWATCH We don't sell agents. We sell the ability to trust them.