AGI Jobs v0 (v2) — Demo → Solving Alpha AGI Governance

An inspectable, offline rehearsal of proposal approval and budget gates, with a bilingual browser workspace and a matching Python evaluator.

Try the bounded proposal rehearsal

This directory now contains an executable offline simulation of a maintenance-budget decision. It checks quorum, approval share, available budget and required owner controls. It reuses the existing governance engine's owner-control coverage calculation. It does not enact governance, authenticate voter identities, read a live electorate, transfer funds or certify a production deployment.

python demo/solving-alpha-agi-governance/run.py
python -m unittest discover -s test/simulation -p test_governance_rehearsal.py -v

The default scenario proposes 420,000 synthetic cents from a 500,000-cent budget. Seventy of ninety cast vote units approve; ninety of one hundred eligible units participate. All four checks pass and the hypothetical remaining budget is 80,000 cents. enacted and settlementApproved remain false. These are illustrative budget units, not a payment.

Change one thing and explain the outcome

Copy scenario.json, edit it and run python demo/solving-alpha-agi-governance/run.py --scenario YOUR_COPY.json:

Raise quorumPercent from 60 to 95: participation fails.
Raise approvalPercent from 67 to 80: approval fails.
Set requestedCents to 500001: available budget fails.
Remove pause from controls: required owner control fails.

The JSON result reports each check and the failing reasons. Abstentions count toward participation and the approval denominator: approval is measured against all cast vote weight, not only approve/reject votes. Arithmetic uses integer cross-products, so exact threshold decisions do not depend on floating-point rounding. A rejected proposal returns exit status 1 and leaves the hypothetical budget unchanged; malformed input returns 2. Duplicate JSON keys, non-finite numbers, unknown fields, duplicate voter identifiers, excess weight, boolean numeric values and unsafe identifiers are rejected. The CLI commits the exact scenario bytes with SHA-256.

This rehearsal treats the supplied votes and control names as scenario data. Real governance requires authenticated identities, authoritative voting power, separately enforced permissions and the target contract's own rules. The related governance workflow and complete demo guide remain available. Existing architecture diagrams below are preserved design context, not evidence that this small simulation operates the wider system.

Use the browser workspace

Serve demo/solving-alpha-agi-governance/web/ with a local static server, then open its index page. For example, run python -m http.server 8080 --bind 127.0.0.1 --directory demo/solving-alpha-agi-governance/web and visit http://127.0.0.1:8080/. The complete Pages build publishes the same workspace at experiments/governance/ when this change is deployed.

Switch between English and French, change one gate, and download the exact scenario or an evidence bundle. Reopening a bundle recomputes its result from the scenario rather than trusting its saved result. The browser labels the hash of its exported scenario downloadedScenarioSha256; the CLI separately hashes the exact bytes it reads. Neither hash proves voter identity or grants authority.

Preserved architectural design

The following overview and systems map describe the wider design, beyond the bounded implementation above.

Overview

Path: demo/solving-alpha-agi-governance/README.md
Module Focus: Anchors Demo → Solving Alpha 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/solving-alpha-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

Architecture diagram · source preserved below

View original Mermaid source

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

Working With This Module

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

Directory Guide

run.py evaluates scenario.json; behavioral acceptance tests live in test/simulation/test_governance_rehearsal.py. The broader design guide and diagram remain below for context.

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.