Production errors surface in Datadog faster than anyone can chase them down. An agent can investigate the moment an error spikes, trace it to the responsible code, and propose a fix while the context is fresh.

How to investigate Datadog production errors with Cursor

• Watches. Fires when a production error is reported in Datadog, via the Datadog trigger or a webhook.
• Investigates. Pulls the error, traces, and logs from Datadog, inspects the repository, and opens a pull request or comment.
• Reports back. A root-cause summary linked to the offending code, escalating to a fix PR when the change is contained.

Prerequisites & integrations

Connect these in your Cursor integrations settings before you enable the automation:

• Datadog

Trigger: Webhook

The prompt

This is the prompt that ships with the template on the Marketplace. Open the template in Automations to load it, then adapt it to your team.

You are an incident-investigation automation focused on Datadog errors.

## Goal

Continuously reduce production errors by investigating high-impact Datadog signals and landing safe fixes.

## Investigation process

1. Use Datadog tools to identify top errors by frequency, user impact, and recency.
2. Group duplicate symptoms into root-cause clusters.
3. Correlate stack traces, service metadata, deployment timing, and relevant code changes.
4. Form a root-cause hypothesis and validate with code evidence.

## Fix policy

- Only implement fixes with high confidence in root cause.
- Prefer minimal, robust changes with low regression risk.
- Add tests where feasible for the failure mode.
- If a safe fix is not possible, provide a concrete follow-up plan.

## Output

If fixed, open a PR and report:
- Error signature(s) addressed
- Root cause
- Fix summary and validation
- Any remaining risk

How to set up automatic production error investigation

1. Open the template in Automations to start from it.
2. Connect the repository the agent should work in.
3. Choose the trigger and connect the tools it uses — Datadog, Webhook.
4. Tune the prompt to your quality bar, then activate the automation.

Tips for customizing error investigation for your services

• Tighten the quality bar. Tell the agent to comment with its findings instead of opening a pull request when it is not confident, so humans stay in the loop on risky changes.
• Send updates where the team works. Add a Slack channel so results land in the flow of work instead of waiting to be noticed.
• Start narrow, then widen. Scope the trigger to one repository, branch, or label first, and expand once you trust the results.
• Make it Team Owned. Set billing to a team-owned automation so runs are shared and visible across the team.