deepsigma 0.6.1

Σ OVERWATCH — Reality Await Layer (RAL) control plane for agentic AI

Institutional Decision Infrastructure

Truth · Reasoning · Memory

🚀 Start Here · 🔁 Hero Demo · 🏢 Boardroom Brief · 📜 Specs · 🗺️ Navigation · 🔬 RAL

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The Problem

Your organization makes thousands of decisions. Almost none are structurally recorded with their reasoning, evidence, or assumptions.

• Leader leaves → their rationale leaves with them.
• Conditions change → nobody detects stale assumptions.
• Incident occurs → root-cause analysis becomes guessing.
• AI accelerates decisions 100× → governance designed for human speed fails silently.

This is not a documentation gap. It is a missing infrastructure layer.

Every institution pays this cost — in re-litigation, audit overhead, governance drag, and silent drift. The question: keep paying in consequences, or invest in prevention.

→ Full economic tension analysis · Boardroom brief · Risk model

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The Solution

Σ OVERWATCH fills the void between systems of record and systems of engagement with a system of decision.

Every decision flows through three primitives:

Primitive	Artifact	What It Captures
Truth	Decision Ledger Record (DLR)	What was decided, by whom, with what evidence
Reasoning	Reasoning Scaffold (RS)	Why this choice — claims, counter-claims, weights
Memory	Decision Scaffold + Memory Graph (DS + MG)	Reusable templates + queryable institutional memory

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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Try It (5 Minutes)

git clone https://github.com/8ryanWh1t3/DeepSigma.git && cd DeepSigma
pip install -r requirements.txt

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

# Full pipeline: episodes → DLR → RS → DS → MG → report
python -m coherence_ops.examples.e2e_seal_to_report

# Why did we make this decision?
python -m coherence_ops iris query --type WHY --target ep-001

Drift → Patch in 60 seconds (v0.3.0):

python -m coherence_ops.examples.drift_patch_cycle
# BASELINE 90.00 (A) → DRIFT 85.75 (B) → PATCH 90.00 (A)

👉 Full walkthrough: HERO_DEMO.md — 8 steps, every artifact touched.

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Golden Path (v0.5.1)

One command. One outcome. No ambiguity. Proves the full 7-step loop end-to-end: Connect → Normalize → Extract → Seal → Drift → Patch → Recall.

# Local (fixture mode — no credentials)
deepsigma golden-path sharepoint \
  --fixture demos/golden_path/fixtures/sharepoint_small --clean

# Or via the coherence CLI
coherence golden-path sharepoint \
  --fixture demos/golden_path/fixtures/sharepoint_small

# Docker
docker compose --profile golden-path run --rm golden-path

Output: golden_path_output/ with per-step JSON artifacts and summary.json.

👉 Details: demos/golden_path/README.md

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Trust Scorecard (v0.6.0)

Measurable SLOs from every Golden Path run. Generated automatically in CI.

python -m tools.trust_scorecard \
  --input golden_path_ci_out --output trust_scorecard.json

# With coverage
python -m tools.trust_scorecard \
  --input golden_path_ci_out --output trust_scorecard.json --coverage 85.3

Output: trust_scorecard.json with metrics, SLO checks, and timing data.

👉 Spec: specs/trust_scorecard_v1.md · Dashboard: Trust Scorecard tab

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Repo Structure

DeepSigma/
├─ START_HERE.md          # Front door
├─ HERO_DEMO.md           # 5-min hands-on walkthrough
├─ NAV.md                 # Navigation index
├── category/             # Economic tension, boardroom brief, risk model
├── canonical/            # Normative specs: DLR, RS, DS, MG, Prime Constitution
├── coherence_ops/        # Python library + CLI + examples
├── specs/                # JSON schemas (11 schemas)
├── examples/             # Episodes, drift events, demo data
├── llm_data_model/       # LLM-optimized canonical data model
├── docs/                 # Extended docs (vision, IRIS, policy packs)
├── mermaid/              # 35+ architecture & flow diagrams
├── engine/               # Compression, degrade ladder, supervisor
├── dashboard/            # React dashboard + mock API
├── adapters/             # MCP, OpenClaw, SharePoint, Power Platform, AskSage, Snowflake, LangChain
├── demos/                # Golden Path end-to-end demo + fixtures
└── release/              # Release readiness checklist

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CLI Quick Reference

Command	Purpose
python -m coherence_ops audit <path>	Cross-artifact consistency audit
python -m coherence_ops score <path> [--json]	Coherence score (0–100, A–F)
python -m coherence_ops mg export <path> --format=json	Export Memory Graph
python -m coherence_ops iris query --type WHY --target <id>	Why was this decided?
python -m coherence_ops iris query --type WHAT_DRIFTED --json	What assumptions decayed?
python -m coherence_ops demo <path>	Score + IRIS in one command
coherence reconcile <path> [--auto-fix] [--json]	Reconcile cross-artifact inconsistencies
coherence schema validate <file> --schema <name>	Validate JSON against named schema
coherence dte check <path> --dte <spec>	Check episodes against DTE constraints
deepsigma golden-path <source> [--fixture <path>]	7-step end-to-end Golden Path

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Connectors (v0.6.0)

All connectors conform to the Connector Contract v1.0 — a standard interface with a canonical Record Envelope for provenance, hashing, and access control.

Connector	Transport	MCP Tools	Docs
SharePoint	Graph API	sharepoint.list / get / sync	docs/26
Power Platform	Dataverse Web API	dataverse.list / get / query	docs/27
AskSage	REST API	asksage.query / models / datasets / history	docs/28
Snowflake	Cortex + SQL API	cortex.complete / embed / snowflake.query / tables / sync	docs/29
LangChain	Callback	Governance + Exhaust handlers	docs/23
OpenClaw	HTTP	Dashboard API client	adapters/openclaw/

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Key Links

Resource	Path
Reality Await Layer (RAL)	ABOUT.md
Front door	START_HERE.md
Hero demo	HERO_DEMO.md
Boardroom brief	category/boardroom_brief.md
Economic tension	category/economic_tension.md
Risk model	category/risk_model.md
Canonical specs	/canonical/
JSON schemas	/specs/
Python library	/coherence_ops/
IRIS docs	docs/18-iris.md
Docs map	docs/99-docs-map.md

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Operations

Resource	Purpose
OPS_RUNBOOK.md	Run Money Demo, tests, diagnostics, incident playbooks
TROUBLESHOOTING.md	Top 20 issues — symptom → cause → fix → verify
CONFIG_REFERENCE.md	All CLI args, policy pack schema, environment variables
STABILITY.md	What's stable, what's not, versioning policy, v1.0 criteria
TEST_STRATEGY.md	Test tiers, SLOs, how to run locally, coverage

Run with coverage:

pytest --cov=coherence_ops --cov-report=term-missing

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Contributing

See CONTRIBUTING.md. All contributions must maintain consistency with Truth · Reasoning · Memory and the four canonical artifacts (DLR / RS / DS / MG).

License

See LICENSE.

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