AGI Jobs v0 (v2) — Demo → AGI Governance

AGI Jobs v0 (v2) is our sovereign intelligence engine; this module extends that superintelligent machine with specialised capabilities for demo/agi-governance.

Overview

• Path: demo/agi-governance/README.md
• Module Focus: Anchors Demo → AGI Governance inside the AGI Jobs v0 (v2) lattice so teams can orchestrate economic, governance, and operational missions with deterministic guardrails.
• Integration Role: Interfaces with the unified owner control plane, telemetry mesh, and contract registry to deliver end-to-end resilience.

Capabilities

• Provides opinionated configuration and assets tailored to demo/agi-governance while remaining interoperable with the global AGI Jobs v0 (v2) runtime.
• Ships with safety-first defaults so non-technical operators can activate the experience without compromising security or compliance.
• Publishes ready-to-automate hooks for CI, observability, and ledger reconciliation.

Systems Map

flowchart LR
    Operators((Mission Owners)) --> demo_agi_governance[[Demo → AGI Governance]]
    demo_agi_governance --> Core[["AGI Jobs v0 (v2) Core Intelligence"]]
    Core --> Observability[[Unified CI / CD & Observability]]
    Core --> Governance[[Owner Control Plane]]

Working With This Module

1. From the repository root run npm install once to hydrate all workspaces.
2. Inspect the scripts under scripts/ or this module's package.json entry (where applicable) to discover targeted automation for demo/agi-governance.
3. Execute npm test and npm run lint --if-present before pushing to guarantee a fully green AGI Jobs v0 (v2) CI signal.
4. Capture mission telemetry with make operator:green or the module-specific runbooks documented in OperatorRunbook.md.

Directory Guide

Key Directories

• alpha-v13
• alpha-v14
• alpha-v15
• alpha-v16
• alpha-v17
• config
• reports
• scripts

Key Files

• RUNBOOK.md
• tsconfig.json

Quality & Governance

• Every change must land through a pull request with all required checks green (unit, integration, linting, security scan).
• Reference RUNBOOK.md and OperatorRunbook.md for escalation patterns and owner approvals.
• Keep secrets outside the tree; use the secure parameter stores wired to the AGI Jobs v0 (v2) guardian mesh.

Next Steps

• Review this module's issue board for open automation, data, or research threads.
• Link new deliverables back to the central manifest via npm run release:manifest.
• Publish artefacts (dashboards, mermaid charts, datasets) into reports/ for downstream intelligence alignment.

Mermaid rendering corrections dated October 8, 2026 are recorded in the historical report manifest. They preserve the original simulation values and timestamps; they do not represent a new execution or new production evidence.

Offline Python mathematical check

Run python demo/agi-governance/run_demo.py --json to recompute the supplied mission's thermodynamic arithmetic and declared owner-control category coverage. This teaching model does not enforce controls or observe a deployed system. log_partition_function is the stable log of the Boltzmann partition sum. shifted_normalization and log_weight_offset expose its log-sum-exp calculation; the shifted sum is not the partition function. partition_function is null when exponentiation would overflow or underflow, with partition_status explaining which case occurred. Expected energy remains computed from normalized weights. When energy levels are omitted (for example, the owner-control-only wrapper), partition metrics are null with status not-supplied; no fictitious energy levels are inserted. The default supplied mission underflows in ordinary floating-point partition space; its finite log-partition is the meaningful output. These are model inputs, not measured physical energy or a governance effectiveness claim.