Databricks Job Failure Agent with Codex

The shape here is copy, count and file. Take the exception class and message out of the run output exactly as Databricks wrote them, count what the recent runs show, then write one Linear issue carrying the error block, the counts and the dates. Codex is strong on exact structured data handling of that kind, and none of it asks for prose.
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Free to start · guided setup · switch agents any time.

Live demo

Watch it work before it's live

Run a staged conversation — no account needed. The agent handles it for real while a simulated world answers its tool calls; nothing touches real accounts, and nothing is actually sent.

nightly_revenue_rollup

Priya Ramanathanacme-prod.cloud.databricks.com / job cluster, Runtime 14.3 LTS

Run 4471028 failed at 02:14 UTC after 6 minutes on task build_revenue_facts. Error: org.apache.spark.sql.AnalysisException: [UNRESOLVED_COLUMN.WITH_SUGGESTION] A column with name `order_total_usd` cannot be resolved. Did you mean one of: [order_total, currency_code]? Line 42, pos 8.

What you'll need

Set up in minutes

Using this template drops you into a guided setup. It asks exactly this, nothing else:

  1. Connect Databricks

    One sign-in. The agent acts through your account, scoped to what this template uses.

  2. Connect Linear

    One sign-in. The agent acts through your account, scoped to what this template uses.

  3. Connect Slack

    One sign-in. The agent acts through your account, scoped to what this template uses.

  4. Runbook notes

    What each job is for and what breaks when it does not run - which jobs are business critical, the tables and dashboards fed by them, who owns each one, and the jobs that are allowed to fail quietly overnight.

  5. Runs on Codex

    Preselected for this page — connect your Codex account during setup, or switch to NoClick's built-in models with one click.

  6. Watch it handle a test run

    A staged conversation against a simulated world — then it’s live.

A considered pairing

Why Codex for this agent

Verbatim, not paraphrased

A stack trace is evidence and a summary of one is a guess. Reproducing it correctly, truncation included, is where a fast and literal harness is the right instrument.

Title built from facts

The job name and the exception class, with no suspected cause smuggled into it. That is what makes the issue findable the next time the same thing breaks.

Good to know

Before you fork

Stack traces often carry table and column names. Where do they end up?

In the model you connect and in the Linear issue it files, so treat that Linear team as the place your error text lives. It reads run output rather than your data, so rows never leave the platform, but a trace can carry schema names and a storage path. If that matters, file into a private team rather than a shared one.

Other editions

Run it with a different agent

claude-codeClaude CodeopencodeopenclawhermesMore Codex agents

Make it yours

Put Databricks Job Failure Agent to work on Codex.

Free to start. Guided setup, a staged test run, and a workflow you can change at every step.