Industrial Optimization Examples

Use this page when the lightweight first proof is complete and you want an advanced example that shows AGILAB as an execution and evidence engine for dynamic industrial AI workflows. These examples live in an optional apps repository project, not in the default hosted first-run path.

What this proves

The reference project is sb3_trainer_project when it is available in your apps repository. It now covers five complementary proof routes:

Example

What it proves

Primary output

Active Mesh Optimization

UAV relays are active agents in a centralized PPO policy; the run shows movement decisions, topology changes, and network delivery evidence.

trainer_uav_active_mesh_ppo

MLflow Auto-Tracking

AGILAB runs the pipeline while MLflow remains the tracking system of record for parameters, metrics, artifacts, and model files.

trainer_uav_relay_queue_ppo_mlflow and mlflow_tracking.json

Multi-App DAG

Flight, satellite, link, network, trainer, and analysis stages exchange explicit artifacts instead of hidden notebook state.

lab_steps.toml and pipeline_view.dot

Resilience / Failure Injection

A degraded relay is injected, then fixed routing is compared with an adaptive policy on the same scenario contract.

trainer_uav_relay_queue_resilience and resilience_summary.json

Train-Then-Serve

A trained policy is loaded, sampled, and exported with a service contract and health gate before a production serving stack is involved.

trainer_uav_relay_queue_service with service_contract.json and service_health.json

How to run it

If you only need the public, deterministic teaching routes, start with the packaged previews:

uv --preview-features extra-build-dependencies run python src/agilab/examples/resilience_failure_injection/preview_resilience_failure_injection.py --output /tmp/resilience_preview.json
uv --preview-features extra-build-dependencies run python src/agilab/examples/train_then_serve/preview_train_then_serve.py --output-dir /tmp/train_then_serve_preview

Use the app-repository route below when you need real trainer artifacts rather than the read-only scenario comparison or service-handoff contract.

  1. Install or link the apps repository that contains sb3_trainer_project.

  2. Open AGILAB, select sb3_trainer_project in PROJECT, then run INSTALL in ORCHESTRATE.

  3. Choose one of the trainer templates in the app args form:

    • Train • UAV Active Mesh • PPO

    • Track • UAV Relay Queue • PPO + MLflow

    • Evaluate • UAV Relay Queue • Resilience

    • Serve • UAV Relay Queue • Policy Service

  4. Open PIPELINE to inspect the recipe and conceptual DAG view.

  5. Open ANALYSIS on the exported allocation, queue, topology, or health artifacts.

What not to claim

  • The active mesh route is a compact centralized-policy teaching example. It is not yet a claim of full decentralized MARL certification.

  • The MLflow route does not create a parallel AGILAB model registry. MLflow remains the tracking and registry integration point.

  • The service route exports a contract and a health sample. It is not a production serving platform by itself.

  • The multi-app DAG is a reproducible artifact contract. It should not be presented as a replacement for hardened production workflow schedulers.

Why it matters

Together these examples demonstrate AGILAB’s intended product position:

  • AGILAB owns setup, environments, workers, execution, DAG context, and operator-facing evidence.

  • MLflow or downstream platforms own long-term tracking, registry, deployment, and production governance.

  • Dynamic industrial systems can be evaluated by comparing baseline, adaptive, failure-injected, and service-contract outcomes through the same artifact layer.