agi-app-mission-decision

agi-app-mission-decision publishes the mission_decision_project AGILAB app as a self-contained PyPI payload. It demonstrates deterministic mission decision evidence rather than a storyboard or slide-only demo.

Purpose

Use this package to run a repeatable decision loop: load a compact mission scenario, score candidate routes, inject an event, re-plan, and export evidence that can be inspected by AGILAB analysis pages.

Installed Project

The distribution name is agi-app-mission-decision; the AGILAB project name is mission_decision_project. The package exposes both mission_decision and mission_decision_project through the agilab.apps entry point group, so AgiEnv(app="mission_decision_project") works without a monorepo checkout.

Install

pip install agi-app-mission-decision

Most users get this package through agi-apps, agilab[ui], or agilab[examples]; direct installation is useful when validating one app package in isolation.

Run In AGILAB

From the UI, select mission_decision_project, open ORCHESTRATE, then run Deploy scheduler & workers and RUN. Open ANALYSIS with the decision-evidence page to inspect strategy deltas and the adaptation timeline.

Expected Inputs

The packaged project includes a deterministic sample scenario. It also includes offline public macro-context fixtures so the default proof does not need a live FRED call, API key, private dataset, or network access.

Expected Outputs

The run writes scenario metrics, generated pipeline evidence, sensor streams, candidate route tables, decision timelines, and reducer summaries under the mission-decision output paths.

Change One Thing

Change a scenario constraint or event severity, then rerun the app. The final decision artifact should show whether the selected strategy and latency/cost deltas changed.

Scope

This is a deterministic R&D app for mission-decision reproducibility. It does not claim live command-and-control integration, production autonomy, or enterprise governance.