Starting point
Application errors rise in the payments service after a deployment.
Production operations solutions
Find out why the software failed.
An alert tells you something has failed, but not why. Operate checks the connected logs, code and records, explains what it found and suggests a change for an engineer to review.
Why this takes time.
An error alert identifies a symptom. Finding the cause still requires someone to reconstruct the release, read logs, check production state and rule out other explanations. That work interrupts the team even when the eventual change is small.
What Operate needs to check.
Start with an alert source and the relevant service repository. Add a database connection where production records help distinguish causes. The investigation depends on available evidence; a plausible explanation still needs review.
Review deployment and access boundariesPayment failures after a release
Illustrative example. Findings depend on connected systems and available evidence. Proposed changes require engineer review.
What the agent checks
- Read the failing request pattern and provider timeout logs.
- Inspect recent changes to the retry path.
- Check whether repeated requests explain connection pool exhaustion.
What the team receives
Engineering receives the evidence and a proposed retry policy. It checks whether the change fits the service and applies it through its normal review process.
Finding
The retry path immediately repeats requests during provider timeouts. The investigation links the log pattern with the relevant code.
payments/retry-policy.patch
- retry: immediate+ retry: exponential_backoff+ max_attempts: 3Illustrative artifact. Review before applying changes.
Try a problem you have already solved.
Connect the systems used in a problem your team has already solved. Ask Operate to investigate it, then compare the answer with yours. Check the evidence and the suggested change before using it for more work.
Explore the production workspaceWhich plan covers this?
Monitor adds proactive error monitoring at $299 per workspace per month. Human-triggered bot investigations are available on Free and Investigate.
Bring your own key or model with no Operate token surcharge. Operate-managed AI usage is additional: $3.00 per million input tokens and $15.00 per million output tokens.
Compare plans and usage termsQuestions about production root cause analysis.
Does Operate replace Datadog or Sentry?
No. Your monitoring tools continue collecting signals and raising alerts. Operate uses connected sources to investigate the cause and propose a change.
Does Operate deploy the fix?
No. Operate presents proposed changes for engineer review and application. Its production connections are read-only.
How should we evaluate the root cause?
Use a known incident, inspect the cited evidence and compare the explanation with your team's findings. Assess the proposed change separately from the explanation.
Related production workflows
Database performance
Use Operate agents to review database performance, investigate expensive query patterns and propose changes with supporting query and code evidence.
Explore this workflowCloud cost review
Operate helps teams review infrastructure usage, identify idle capacity and investigate cost recommendations with connected evidence.
Explore this workflowProduction security review
Review production access configuration with Operate agents. Investigate exposure risks and inspect proposed configuration changes before applying them.
Explore this workflowSlack and Teams investigations
Use Operate in Slack or Microsoft Teams to investigate unexpected product behavior using logs, code and production data, with evidence and proposed next steps.
Explore this workflowProduction knowledge library
Operate automatically generates verified knowledge articles from root causes. Teams can manually manage, edit and verify articles for future production investigations.
Explore this workflowNeed engineers to help handle the work? Explore managed operations.