Advanced Proof Pack

Use this route after the lightweight hosted demo or the local first proof. It surfaces stronger AGILAB capabilities that are already packaged but are too deep for the default Hugging Face first-run path.

The goal is not to make the first demo longer. The goal is to keep the newcomer path clean while giving evaluators a second pack that proves richer behavior: mission decisions, execution-model benchmarks, network simulations, service health, app-to-app contracts, connector contracts, and release evidence.

What belongs here

Proof route

What it proves

Where to start

data_io_2026_project

Deterministic mission-data decision loop: ingest evidence, score routes, inject an event, re-plan, and export a decision bundle.

Select the built-in app, run ORCHESTRATE, then open view_data_io_decision.

execution_pandas_project / execution_polars_project

Execution-model benchmarking: AGILAB pool/Dask/Cython choices are measured separately from dataframe-library choice.

Use Execution Playground.

uav_queue_project / uav_relay_queue_project

Network-style experiment analysis: queue buildup, packet drops, routing policy changes, topology, trajectories, and generic network map views.

Select the built-in app, run it, then open view_uav_queue_analysis, view_uav_relay_queue_analysis, or view_maps_network.

inter_project_dag packaged preview

App-to-app artifact handoff without private infrastructure: one app produces an explicit artifact contract that another app can consume.

Run src/agilab/examples/inter_project_dag/preview_inter_project_dag.py.

mlflow_auto_tracking packaged preview

Optional experiment memory: AGILAB writes local evidence first, then logs params, metrics, and artifacts through MLflow when the backend is present.

Run src/agilab/examples/mlflow_auto_tracking/preview_mlflow_auto_tracking.py.

resilience_failure_injection packaged preview

Resilience comparison: inject one relay degradation event, then compare fixed, replanned, search-based, and active-policy responses on the same scenario contract.

Run src/agilab/examples/resilience_failure_injection/preview_resilience_failure_injection.py.

train_then_serve packaged preview

Service handoff: freeze the trained-policy artifact path, IO contract, prediction sample, and health gate before a serving stack is started.

Run src/agilab/examples/train_then_serve/preview_train_then_serve.py.

service_mode packaged preview

Persistent worker lifecycle and health gates: start, status, health, and stop are presented as explicit operator actions.

Run src/agilab/examples/service_mode/preview_service_mode.py and see Service Mode.

Data connector reports

Connector catalogs, local/cloud/search endpoint shapes, health planning, and credential-safe resolution reports without requiring live secrets.

See Data Connectors.

Release proof

Public trust evidence: PyPI package, GitHub release, CI guardrails, docs-source integrity, and hosted-demo evidence in one page.

See Release Proof.

Optional SB3 industrial optimization examples

Advanced app-repository routes for active mesh optimization, MLflow auto-tracking, multi-app DAGs, resilience/failure injection, and train-then-serve policy contracts.

See Industrial Optimization Examples when sb3_trainer_project is installed.

How to demo it

Keep the story bounded. Do not switch apps randomly. Use one of these lanes:

Decision lane

data_io_2026_project -> ORCHESTRATE -> ANALYSIS -> view_data_io_decision. Stop when the selected strategy, re-plan event, decision deltas, and exported artifacts are visible.

Performance lane

Execution Playground. Stop when the viewer understands that pool is AGILAB’s external local fan-out, Pandas can benefit from process fan-out, and Polars may not because it already owns native internal parallelism.

Network lane

uav_relay_queue_project -> ORCHESTRATE -> ANALYSIS -> view_uav_relay_queue_analysis plus view_maps_network. Stop when queue buildup, relay choice, drops, topology, and trajectories are visible from the same run artifacts.

Operator lane

service_mode preview and Service Health JSON Schema. Stop when the lifecycle and health thresholds are explicit. Do not claim production service certification from the preview.

Tracking lane

mlflow_auto_tracking preview. Stop when the local evidence bundle and the tracking status show the same params, metrics, and artifact path. If MLflow is not installed, the expected status is skipped with an installation hint.

Resilience lane

resilience_failure_injection preview. Stop when the fixed route degrades, the post-failure route ranking is explicit, and the adaptive response wins without implying a certified MARL benchmark.

Train-then-serve lane

train_then_serve preview. Stop when the service contract, prediction sample, and health payload are written without implying that AGILAB is a production serving platform.

Industrial optimization lane

Industrial Optimization Examples. Stop when the reviewer can see the active-mesh or queue-routing artifact contract, optional MLflow tracking status, resilience comparison, or service contract without confusing those examples with the default hosted first proof.

What not to claim

  • Do not present the Advanced Proof Pack as the default hosted demo.

  • Do not claim live cloud validation unless the compatibility matrix says that route is validated.

  • Do not claim Cython end-to-end speedup from the kernel-only benchmark; the full run still includes IO, grouping, artifacts, startup, and orchestration.

  • Do not claim Polars should always benefit from AGILAB pool mode. Polars already manages native internal parallelism.

  • Do not claim the UAV examples are full research simulators. They are compact public scenarios shaped to prove AGILAB workflow value.

  • Do not claim the Active Mesh Optimization example is full decentralized MARL. It is a compact centralized-policy route that makes the agent/action contract visible for later hardening.

  • Do not present mlflow_auto_tracking as an AGILAB model registry. It is an adapter proof that keeps MLflow as the tracking and registry system when used.