Audit operational alert decision integrity

What it's for

Makes Gitrevio's context-cause-action, cooldown, severity routing and acknowledgement claims independently auditable instead of assuming a delivered notification was correct or useful.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "alert_evaluations": [
    {
      "aggregate_unit_id": "team-a",
      "alert_class_id": "delivery-risk",
      "alert_fired": true,
      "channel_id": "slack",
      "condition_episode_id": "episode-1",
      "eligible": true,
      "evaluated_at_ms": 1000,
      "evidence_snapshot_hash": "snapshot-1",
      "evidence_verified": true,
      "id": "evaluation-1",
      "observed_data_age_ms": 100,
      "outcome_due_at_ms": 1500,
      "policy_id": "delivery-v1",
      "risk_score": 0.9,
      "routed_recipient_role_ids": [
        "team-lead"
      ],
      "satisfied_context_ids": [
        "trajectory",
        "blockers"
      ],
      "satisfied_control_ids": [
        "tenant-scope"
      ],
      "severity_id": "p1",
      "source_system_id": "alert-engine",
      "suppression_reason_id": null
    }
  ],
  "alert_events": [
    {
      "acknowledged_at_ms": 1100,
      "action_id": "redistribute-review",
      "action_started_at_ms": 1200,
      "evaluation_id": "evaluation-1",
      "evidence_verified": true,
      "fired_at_ms": 1010,
      "id": "alert-1",
      "resolved_at_ms": 1400
    }
  ],

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "configuration": {
    "as_of_ms": 2000,
    "minimum_verified_evaluation_fraction": 0.95
  },
  "decision": "pass",
  "evaluation_diagnostics": [
    {
      "alert_class_id": "delivery-risk",
      "evaluation_id": "evaluation-1",
      "expected_fired": true,
      "failed_gates": [],
      "passed": true,
      "policy_id": "delivery-v1",
      "recorded_fired": true
    }
  ],
  "failed_gates": {
    "evaluation_failure_counts": {},
    "orphan_alert_event_ids": [],
    "orphan_outcome_ids": []
  },
  "guardrails": [
    "This function audits submitted alert-decision evidence; it does not establish causality, infer intent, or prove every material condition was observable.",
    "Named-person alerts and employment/security actions require separate lawful purpose, proportionality, human review, correction rights and access controls.",
    "Passing never authorizes a notification, escalation, investigation, access change, employment action or production intervention."
  ],
  "method": "point_in_time_operational_alert_decision_evidence_audit_v1",
  "summary": {
    "alert_event_count": 1,
    "evaluation_count": 1,
    "fired_evaluation_count": 1,
    "mature_outcome_count": 1,
    "verified_evaluation_count": 1,
    "verified_evaluation_fraction": 1
  }
}

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze independently reviewed alert-policy versions, eligibility, score thresholds, context/control/routing requirements, permitted suppressions, cooldown, freshness and response clocks at the as-of cutoff.
2. 2 Recompute each evaluation's expected fire/suppress decision, validate a cooldown only against an earlier alert in the same policy/unit/condition episode, and reconcile fired decisions one-to-one to immutable alert events.
3. 3 Verify recipient role, severity/channel, timely acknowledgement, required action and due mature outcome evidence; expose orphan events/outcomes and return repair gates rather than an actionability claim.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "audit every pointintime operational alert evaluation" }
  → finds "audit_operational_alert_decision_integrity"

gitrevio_capability_describe
  { "capability_id": "audit_operational_alert_decision_integrity" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_operational_alert_decision_integrity", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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