How an AI agent can combine PlanetScale database context and Sentry error evidence into an incident brief with hypotheses, validation checks, and approval steps
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant PlanetScale context and matches it with Sentry, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent combine PlanetScale database context and Sentry error evidence into an incident brief with hypotheses, validation checks, and approval steps?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A PlanetScale and Sentry database incident agent inspects database and application evidence together. PlanetScale supplies database branch, schema, deployment, query, and Insights context. Sentry supplies errors, traces, release timing, affected transactions, and environment context that can show where database behavior reaches users.
When to use this workflow
Use it for latency spikes, query-related exceptions, regressions after a schema deployment, or application failures that may depend on database state. Correlation is not causation: the agent should preserve competing hypotheses until a query, trace, or controlled test supports one.
How PlanetScale and Sentry give the agent context
Connect both plugins and provide the incident window, environment, affected service, and relevant PlanetScale database or branch. Sentry should establish the user-visible failure and transaction path. PlanetScale should show query or schema evidence during the same window. The agent should not deploy, roll back, or change database configuration without explicit approval.
Example starter prompt
Investigate Sentry issue [issue ID] against PlanetScale evidence for [database or branch] during [time window]. Build a shared timeline, identify implicated queries or schema changes, and separate confirmed facts from hypotheses. Do not deploy, roll back, or change database settings.Suggested workflow steps
Start with the Sentry error path, affected release, and timestamps. Compare that timeline with PlanetScale schema deployments and query evidence. Check identifiers, environment, and time zones before treating records as related, then propose the smallest safe test for each plausible cause.
Questions this workflow answers
Did a recent database change cause the application errors users started seeing?
The agent builds two timelines before it proposes an answer. The application side includes first seen, last seen, affected release, transaction, stack location, environment, and error volume. The database side includes schema deploys, branch or deploy-request identity, query fingerprints, latency or row changes, and the exact database environment. Time zones and release identifiers must line up; “both happened Tuesday” is weak evidence.
It then traces the failing request far enough to identify the database interaction involved. A missing column error may point directly to release ordering. A timeout can also come from changed traffic, a slow downstream call, lock contention, or an application retry loop. For every hypothesis, the agent lists supporting evidence, contradicting evidence, and the smallest test that could separate it from alternatives. A query becoming slower after deployment remains a correlation until a comparison or controlled reproduction explains why.
Mitigation options are ranked by reversibility and user impact. The team might disable a code path, roll back application code, pause a rollout, or prepare a schema follow-up. The agent must state what each option changes, what data risk remains, and which owner can approve it. A schema rollback is not described as safe if the change already transformed or removed data.
The incident brief gives responders one shared timeline, affected paths, query and trace links, release and schema identifiers, hypotheses, tests, and approval-required mitigations. Sensitive query values and user data stay redacted. Engineering decides the cause and action after reviewing the evidence; the agent does not deploy, edit database settings, resolve the incident, or mark an error fixed.
Expected handoff
Ask for a shared timeline, affected transactions, implicated queries or schema changes, evidence for and against each hypothesis, next checks, owners, and approval-required mitigation options.