{
  "id": "databricks-mlops-agent",
  "name": "Databricks MLOps Agent",
  "version": "0.1.0",
  "type": "agent",
  "provider": "databricks",
  "harnesses": [
    "codex",
    "copilot",
    "claude-code",
    "cursor",
    "gemini",
    "kiro"
  ],
  "summary": "Expert review of machine-learning model lifecycle on Databricks: MLflow 3 with Unity Catalog as the default registry namespace, alias-based promotion (Champion, Challenger) over legacy stages, feature-store design with FeatureEngineeringClient and point-in-time correctness, Model Serving endpoint configuration (traffic splits, provisioned concurrency, scale-to-zero), inference-table auto-logging with at-least-once guarantees, batch inference with `ai_query()`, and cross-environment model promotion mechanics. Establishes evidence chains linking tests to production deployments.",
  "source_type": "original",
  "official_docs": [
    "https://docs.databricks.com/aws/en/machine-learning/manage-model-lifecycle/",
    "https://docs.databricks.com/aws/en/mlflow/",
    "https://docs.databricks.com/aws/en/mlflow/model-registry-3",
    "https://docs.databricks.com/aws/en/machine-learning/model-serving/",
    "https://docs.databricks.com/aws/en/machine-learning/model-serving/inference-tables",
    "https://docs.databricks.com/aws/en/machine-learning/feature-store/uc/feature-tables-uc",
    "https://docs.databricks.com/aws/en/machine-learning/automl/"
  ],
  "security_notes": "Static review of model lifecycle configuration, promotion logic, and registry schema. Reads MLflow model URIs, alias assignments, feature-store metadata, serving-endpoint configuration, and Model Serving traffic splits; never executes model inference, never modifies a production registry, never invokes a serving endpoint, never accesses inference results tied to customer data. Assumes Unity Catalog governance on model and feature data; if governance is absent or unenforced, escalates to the governance specialist. A claim about cross-account promotion without named approval carries a risk escalation tag.",
  "last_verified": "2026-08-17",
  "path": "agents/databricks/databricks-mlops-agent/",
  "harness_variants": {
    "codex": "agents/databricks/databricks-mlops-agent/harnesses/codex.toml",
    "copilot": "agents/databricks/databricks-mlops-agent/harnesses/copilot.agent.md",
    "claude-code": "agents/databricks/databricks-mlops-agent/harnesses/claude-code.agent.md",
    "cursor": "agents/databricks/databricks-mlops-agent/harnesses/cursor.agent.md",
    "gemini": "agents/databricks/databricks-mlops-agent/harnesses/gemini.agent.md",
    "kiro-ide": "agents/databricks/databricks-mlops-agent/harnesses/kiro-ide.agent.md",
    "kiro-cli": "agents/databricks/databricks-mlops-agent/harnesses/kiro-cli.agent.json"
  },
  "companion_skills": [
    "databricks-mlops"
  ],
  "execution_tier": "static-review",
  "lifecycle": "experimental",
  "author": "github: VincentChuWaiChow",
  "routing_keywords": [
    "mlflow",
    "model registry",
    "models in unity catalog",
    "model alias",
    "champion",
    "challenger",
    "model serving",
    "serving endpoint",
    "feature store",
    "feature table",
    "inference table",
    "ai_query",
    "automl",
    "model promotion"
  ]
}