Forecast agentic action loss

What it's for

Shows CTOs, boards and investors the locally calibrated downside of agentic operations—including common control failures and incomplete recovery chains—instead of borrowing generic AI incident rates.

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.

{
  "business_assets": [
    {
      "evidence_verified": true,
      "id": "payments",
      "value_at_risk": 100
    }
  ],
  "current_action_plans": [
    {
      "action_class": "deployment",
      "asset_id": "payments",
      "asset_loss_fraction_if_terminal_loss": 0.8,
      "evidence_verified": true,
      "id": "deploy-service",
      "planned_action_count": 2
    },
    {
      "action_class": "deployment",
      "asset_id": "payments",
      "asset_loss_fraction_if_terminal_loss": 0.6,
      "evidence_verified": true,
      "id": "deploy-worker",
      "planned_action_count": 1
    }
  ],
  "historical_loss_events": [
    {
      "action_class": "deployment",
      "direct_loss": 10,
      "evidence_verified": true,
      "id": "agentic-loss-0"
    },
    {
      "action_class": "deployment",
      "direct_loss": 15,
      "evidence_verified": true,
      "id": "agentic-loss-1"
    },
    {
      "action_class": "deployment",
      "direct_loss": 20,
      "evidence_verified": true,
      "id": "agentic-loss-2"

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.

{
  "action_class_diagnostics": [
    {
      "action_class": "deployment",
      "historical_loss_event_count": 3,
      "historical_transition_count": 130,
      "loss_support_sufficient": true,
      "mean_terminal_loss_probability_per_action": 0.08107543,
      "mean_unresolved_probability_after_chain_limit": 0.00000543,
      "observed_transient_states": [
        "contained",
        "deviated",
        "executing",
        "recovered"
      ],
      "posterior_direct_loss_median": 14.4225,
      "transition_support_sufficient": true
    }
  ],
  "action_plan_diagnostics": [
    {
      "action_class": "deployment",
      "action_plan_id": "deploy-service",
      "asset_id": "payments",
      "planned_action_count": 2,
      "posterior_mean_terminal_loss_probability_per_action": 0.08107543
    },
    {
      "action_class": "deployment",
      "action_plan_id": "deploy-worker",
      "asset_id": "payments",
      "planned_action_count": 1,
      "posterior_mean_terminal_loss_probability_per_action": 0.08107543
    }
  ],
  "asset_diagnostics": [
    {
      "asset_id": "payments",
      "expected_loss": 21.42,
      "probability_of_any_loss": 0.26,
      "value_at_risk": 100
    }
  ],
  "assumptions": [

How it works

Markov & state-space control — Choose a policy over evolving states when today's action changes tomorrow's options.

1. 1 Build complete class-by-transition counts including zeros for every transient action state and fit structurally masked Dirichlet posteriors with tenant-level pooling plus partially pooled lognormal direct-loss marks.
2. 2 Draw coherent operating scenarios and one shared control-failure regime per simulation, adjust adverse/containment/recovery transitions, and propagate each posterior chain to completion, loss or explicit unresolved mass.
3. 3 Simulate planned action volumes, union terminal failures across plans reaching the same asset, add non-overlapping direct loss, and gate VaR/CVaR on local transition, severity, evidence and absorption support.

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": "forecast expected and tail operational aiagent" }
  → finds "forecast_agentic_action_loss"

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

gitrevio_capability_run
  { "capability_id": "forecast_agentic_action_loss", "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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