Manual
The agent creates the finding, savings estimate, Jira context, and recommended action. A human decides whether to apply.
Autonomous remediation
Close the Databricks operations loop without losing control.
Omnitrace is built for the work after detection: decide what matters, route the evidence, apply the fix inside guardrails, and prove the outcome.
50+
detector types
19
auto-fix paths
3-5 min
apply-to-verify target
Operating model
The agent does the repeatable work. Humans keep the policy.
Databricks remediation usually stalls because every finding becomes a ticket, every ticket needs context, and every context switch competes with product work. Omnitrace packages the evidence and moves only the right action to the right autonomy level.
Auto low-risk
Safe fixes such as idle termination or warehouse auto-stop can run automatically inside dollar and blast-radius limits.
Approval required
Higher-risk changes move through review with full evidence, owner context, and verification criteria.
Closed loop
From signal to verified outcome.
Step 1
Detect waste, drift, or reliability risk
Step 2
Explain the root cause in platform language
Step 3
Estimate savings and operational impact
Step 4
Open or update Jira with evidence
Step 5
Apply the approved fix through MCP tools
Step 6
Verify the change and persist the audit trail
Review governed remediation.
Start from a Databricks pain point and see how Omnitrace detects it, drafts the Jira context, applies an approved fix, and verifies the result.