Workflow Examples
Common automation recipes for experimentation, evaluation, and deployment.
Reinforcement fine-tuning loop
Combine evaluator agents with gradient updates by defining a `reward_strategy` block in the workflow YAML.
Multi-modal pipeline
Chain embedding, reasoning, and generation agents with `inputs: [vision, text]` workloads to orchestrate multi-modal research.
Compliance-ready deployment
Attach review policies to workflows so every promotion to production runs through governance checks and human-in-the-loop approvals.
Shadow deployments
Mirror production traffic into experimental models by adding a `shadow_routes` stanza; DeepBox automatically aggregates comparisons and will fail over if anomalies spike.
Offline evaluation packs
Bundle test corpora, heuristics, and evaluation notebooks into versioned packs so every release ships with reproducible verification steps.
Hybrid human feedback
Configure workflows to pause at critical checkpoints, ping human reviewers, and resume automatically once approvals are logged.
Continuous retraining loop
Schedule workflows with cron expressions so agents ingest fresh data, retrain models, and run drift checks without manual intervention.
Edge deployment recipe
Use lightweight agents bundled via WebAssembly to deploy experiments on air-gapped or on-premise clusters while still reporting metrics back to DeepBox Studio.
A/B experimentation
Run multi-track experiments by routing 50% of inference calls to workflow A and 50% to workflow B, then let DeepBox automatically promote the top performer.
Placeholder checklist
Extra placeholder text so you can evaluate scrolling behavior for long-form workflow docs. link stubs, bullet lists, and callouts can live here later.