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Your on-call agent investigates production incidents by searching Sentry and correlating observability data. This recipe shows the simplest useful instrumentation: one run, one guarded tool call, Monitor mode. Source: incident-remediation-agent.ts · incident_remediation_agent.py

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Scenario

An incident remediation agent receives an alert about elevated error rates. It searches Sentry for related issues and reports findings. You want this traced in Apie before adding guardrails.

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Step 1 — Create the client

Monitor mode is the default — guardrails evaluate but never block. 
const apie = await Apie.create({
  agent: {
    key: "incident-remediation-agent",
    name: "Incident Remediation Agent",
  },
  runtime: { environment: process.env.NODE_ENV ?? "development" },
  mode: "monitor",
});

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Step 2 — Start a run

One investigation = one run. 
const run = await apie.runs.start({
  inputSummary: "Investigate production error rate spike",
});
Use withCanonicalToolAction to map the Sentry call to consistent metadata: 
await withCanonicalToolAction(apie, {
  runId: run.id,
  toolName: "sentry.search_issues",
  provider: "sentry",
  actionType: "read",
  resourceType: "incident",
  environment: "production",
  riskLevel: "medium",
  metadata: { service: "api" },
}, async () => {
  // Perform Sentry search here
});

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Step 4 — Complete the run

await apie.runs.complete(run.id, {
  status: "completed",
  metadata: { outputSummary: "Incident narrowed to release rollback candidate." },
});
await apie.flush();
await apie.shutdown();

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What you’ll see

A single run in the dashboard with one tool call (sentry.search_issues), action type read, resource type incident, and your output summary.

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What to try next

  1. Declare capabilities for sentry.search_issues
  2. Run in monitor mode with deploy/vault scenarios from Guardrail packs
  3. Add more tool calls as your agent’s capabilities grow

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Next steps

Platform connectors

Sentry, Datadog, PagerDuty helpers.

Production release gate

Pipeline with risky actions.