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DeepSigma

DeepSigma prevents decision amnesia in AI systems.

Log every agent decision. Detect when it drifts. Prove what happened.

Quickstart

pip install deepsigma

# Log an agent decision
coherence agent log decision.json

# Audit all logged decisions
coherence agent audit --json

# Coherence score
coherence agent score

60-Second Proof

coherence demo

BASELINE   90.00 (A)
DRIFT      85.75 (B)   red=1
PATCH      90.00 (A)   patch=RETCON  drift_resolved=true

Three states, deterministic every run:

1. BASELINE — sealed episode, coherence scored
2. DRIFT — data changed, drift detected automatically
3. PATCH — governed retcon applied, coherence restored

Machine-readable: coherence demo --json

What It Does

DeepSigma is the institutional memory layer that makes AI decisions reconstructable.

Every agent decision becomes a sealed, hash-chained episode. Drift between decisions is detected automatically across 8 types. Authority is captured cryptographically, not implied. The full "why" is retrievable in under 60 seconds.

In practice:

• the "why" is retrievable (not tribal)
• authority is explicit (not implied)
• changes are patched, not overwritten
• drift is detected early and corrected consistently

Editions

One product line, one version, two editions:

• CORE edition: minimal, demo-first, deterministic (pip install deepsigma)
• ENTERPRISE edition: extended adapters, dashboards, and ops surfaces (repo-native under enterprise/)

Edition boundary ledger: EDITION_DIFF.md

Operating Modes

Core Mode

Use Core mode when you need fast adoption and low cognitive load.

Active Core surface at repo root:

• run_money_demo.sh
• src/core/
• docs/examples/demo-stack/
• tests/test_money_demo.py

Enterprise Mode

Use Enterprise mode when you need connectors, dashboards, extended security, broader telemetry, and integration-heavy workflows.

Dependency note:

• pip install "deepsigma[enterprise]" installs enterprise runtime extras used by telemetry/radar tooling.
• Full enterprise code surfaces are repository-native under enterprise/ and are run from source in this repo.

Enterprise surfaces are first-class under:

• enterprise/README.md

Examples of parked modules:

• enterprise/dashboard/
• enterprise/docker/
• enterprise/release_kpis/
• enterprise/schemas/
• enterprise/scripts/
• enterprise/src/ (non-core packages)
• enterprise/docs/ (full enterprise docs)

Run the enterprise wedge:

make enterprise-demo
make test-enterprise

Primitive Loop: CERPA

Claim -> Event -> Review -> Patch -> Apply

CERPA is the foundational adaptation loop for the platform. Every governance flow — across IntelOps, ReOps, FranOps, AuthorityOps, and ActionOps — follows this cycle:

1. Claim — an asserted truth or commitment
2. Event — an observable occurrence
3. Review — evaluate the claim against the event
4. Patch — corrective action if drift is detected
5. Apply — execute the patch and update state

python -m src.core.examples.cerpa_contract_demo
python -m src.core.examples.cerpa_agent_supervision_demo

Release Artifacts

Build both edition artifacts from one version line:

make release-artifacts

Outputs in dist/:

• deepsigma-core-vX.Y.Z.zip
• deepsigma-enterprise-vX.Y.Z.zip

Full Platform References

For the full-platform docs and architecture map, use parked docs directly:

• enterprise/docs/positioning/positioning_manifesto.md
• enterprise/docs/positioning/executive_briefing_one_page.md
• enterprise/docs/release/
• enterprise/docs/security/
• enterprise/docs/mermaid/

Repo Snapshot (auto-generated 2026-03-07 04:23 UTC)

• 1,732 files | 318,085 lines of code
• 41 CI workflows | 163 test files | 5 pyproject.toml
• 21 EDGE modules

LOC by extension: .py 105,380 .html 59,838 .json 56,166 .md 47,787 .svg 28,054 .jsonl 5,278 .tsx 2,942 .ttl 2,804 .patch 1,438 .ts 1,244

Repo Intent

• Keep root focused on a reliable first proof.
• Keep enterprise depth available without deleting capability.
• Expand from Core into Enterprise intentionally, not by drift.

License

MIT