Summary
The CLI adds `databricks environments setup-local` to provision matched Python environments for Databricks compute targets and extends `aitools install` to support Gemini CLI and Pi. Bundles fix the ignored `bundle.deployment.lock.force` setting, add pipeline `cascade_on_destroy` control, improve experimental `job_runs` with idempotency tokens and completion waiting, and add UC secrets resource support.
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More from databricks/cli
* Bump `github.com/databricks/databricks-sdk-go` from v0.166.0 to v0.170.0 ([#6251](https://github.com/databricks/cli/pull/6251)).
The Terraform deployment engine is now deprecated; migrate to the direct deployment engine for continued support. AI runtime tasks now automatically package local directories into tarballs during deployment, and the direct engine fixes several convergence issues with empty grants, webhook notification ordering, pipeline configuration, and vector search index creation.
SSH connect now supports specifying serverless usage policies via --usage-policy-id, and bundle deploy/destroy are more robust against transient app states and full workspaces. Bundle validation is stricter for grants while empty field values are now dropped, fixing deployment failures and spurious permission drift.
The `databricks genie ask` command is now stable (promoted from experimental), enabling practitioners to ask natural-language questions about their data directly from the CLI. Multiple reliability and usability improvements ship, including auth profile validation timeouts, fixes for GCS-backed UC Volumes deletion, bundle validation for sql_warehouse configurations, opt-in spark_python_task file downloads in bundle generation, and instance_pools resource support in Declarative Automation Bundles.
Bundles now auto-migrate from Terraform to the direct engine when configured and the dry-run is clean, while a new `env:` section in scripts lets you declare environment variables that reference bundle, workspace, and variable values. SSH interactive sessions now properly resolve Python and pip to the environment interpreter instead of system libraries, and bundle init provides clearer error messages for unsupported template URL protocols.
Profile-based authentication now takes precedence over environment variables, and deployment bugs affecting PyDABs-generated permissions and resource grants with --select have been fixed. New experimental job_runs resources are available, along with UC volume path support and options to hard-delete Lakebase branches or assume control of existing postgres databases on bundle deploy.