Forecast AI configuration regression loss
Forecast material AI configuration regression, rollback-capped request exposure, excess failures, net value and economic-loss VaR/CVaR from tenant-local concurrent control/candidate evidence, partially pooled lognormal severity, coherent operating scenarios and a shared platform-regression state.
What it's for
Shows the board-facing downside of AI configuration changes: probability of real regression, requests exposed before rollback, excess failures and correlated economic tail—not merely an offline score delta.
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.
| Field | Type | Role | Required |
|---|---|---|---|
| current_releases | array of objects (16 fields) | Evidence | Yes |
| historical_release_cohorts | array of objects (10 fields) | Evidence | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_historical_releases | integer ≥ 1, ≤ 1000 | Your calibration | Optional |
| scenarios | array of objects (8 fields) | Evidence | Yes |
| seed | integer ≥ 0 | Numerical control | Optional |
| simulations | integer ≥ 200, ≤ 100000 | Numerical control | Optional |
| tail_probability | number > 0, ≤ 0.5 | Your calibration | Optional |
Each current_releases
record
| Field | Type | Required |
|---|---|---|
| baseline_failure_count | integer (≥ 0) | Yes |
| baseline_observation_count | integer (≥ 0) | Yes |
| change_class | string (non-empty) | Yes |
| evaluation_failure_count | integer (≥ 0) | Yes |
| evaluation_observation_count | integer (≥ 0) | Yes |
| evidence_verified | boolean | Yes |
| failure_loss_prior_log_mean | number | Yes |
| failure_loss_prior_log_sd | number (> 0) | Yes |
| failure_prior_alpha | number (> 0) | Yes |
| failure_prior_beta | number (> 0) | Yes |
| fixed_rollout_cost | number (≥ 0) | Yes |
| forecast_request_count | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| maximum_failure_rate_increase | number (≥ 0, ≤ 1) | Yes |
| rollback_exposure_limit_requests | integer (≥ 1) | Yes |
| value_per_avoided_failure | number (≥ 0) | Yes |
{
"current_releases": [
{
"baseline_failure_count": 5,
"baseline_observation_count": 100,
"change_class": "prompt",
"evaluation_failure_count": 5,
"evaluation_observation_count": 100,
"evidence_verified": true,
"failure_loss_prior_log_mean": 4.605170185988092,
"failure_loss_prior_log_sd": 0.5,
"failure_prior_alpha": 1,
"failure_prior_beta": 19,
"fixed_rollout_cost": 100,
"forecast_request_count": 10000,
"id": "support-v3",
"maximum_failure_rate_increase": 0.02,
"rollback_exposure_limit_requests": 500,
"value_per_avoided_failure": 100
}
],
"historical_release_cohorts": [
{
"candidate_failure_count": 6,
"candidate_request_count": 100,
"change_class": "prompt",
"control_failure_count": 5,
"control_request_count": 100,
"evidence_verified": true,
"failure_loss_log_squared_sum": 127.24555465148158,
"failure_loss_log_sum": 27.63102111592855,
"failure_loss_observation_count": 6,
"id": "prompt-release-0"
},
{
"candidate_failure_count": 6,
"candidate_request_count": 100,
"change_class": "prompt",
"control_failure_count": 5,
"control_request_count": 100,
"evidence_verified": true,
"failure_loss_log_squared_sum": 127.24555465148158,
"failure_loss_log_sum": 27.63102111592855,
"failure_loss_observation_count": 6,Truncated for display — the full payload is 96 lines.
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.
{
"assumptions": [
"The release registry includes non-regressing changes and failed requests; control and candidate cohorts are concurrent or otherwise exchangeable, use the same mature failure definition and preserve the evaluated configuration identity.",
"Baseline and candidate failure use tenant-local Beta posteriors; positive failure severity uses a partially pooled lognormal model; rollback caps exposure only after a material regression state is reached.",
"Every simulation shares one operating scenario and common platform-regression state across releases, preserving correlated downside rather than summing independent average forecasts.",
"This conditional release forecast is not causal proof, a safety certificate, an SLA, deployment approval or a judgment about a prompt author, employee, team, provider or country."
],
"configuration": {
"minimum_historical_releases": 4,
"seed": 13,
"shared_common_regression_state": true,
"shared_scenario_draws": true,
"simulations": 300,
"tail_probability": 0.05
},
"decision": "ai_configuration_regression_forecast_supported",
"failed_gates": [],
"method": "tenant_bayesian_ai_configuration_regression_loss_v1",
"release_forecasts": [
{
"change_class": "prompt",
"economic_loss_conditional_value_at_risk": 48027.5661,
"economic_loss_value_at_risk": 19798.3585,
"expected_economic_loss": 7469.103,
"expected_excess_failures": 57.3467,
"expected_exposed_requests": 6647.25,
"expected_net_value": -5475.7697,
"historical_release_count": 4,
"posterior_baseline_failure_probability": 0.0502,
"posterior_candidate_failure_probability": 0.0882,
"probability_material_regression": 0.3533,
"release_id": "support-v3"
}
],
"scenario_diagnostics": [
{
"configured_probability": 0.8,
"expected_portfolio_economic_loss": 5246.8828,
"realized_draw_count": 239,
"scenario_id": "base"
},
{
"configured_probability": 0.2,
"expected_portfolio_economic_loss": 16175.8348,Truncated for display — the full payload is 62 lines.
How it works
Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.
- 1 Retain the complete local release registry including non-regressions and aggregate control/candidate failures plus positive loss log moments within stable change classes.
- 2 Draw tenant-local Beta failure posteriors and partially pooled lognormal severity, then apply one coherent scenario and common platform-regression state across all current releases.
- 3 Cap request exposure when a material regression triggers governed rollback, compare candidate failures against the same-draw baseline counterfactual and report release plus portfolio loss VaR/CVaR with sparse-support abstention.
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.
Assumptions & guardrails
- Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
- Control and candidate cohorts are concurrent or exchangeable, outcomes are mature and identical, configuration identity is preserved, rollback detection is executable and release classes remain stable.
- A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
- This is a conditional release forecast, not causal proof, safety certification, an SLA, deployment authority or author/team/provider scoring.
Minimum evidence
- historical_release_cohorts: required and organization-defined
- current_releases: required and organization-defined
- scenarios: required and organization-defined
How to validate it
Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- stable change-class release registry joined to concurrent control/candidate mature outcomes, rollback detection/exposure, incident loss and finance marks without dropping zero-failure or rejected releases
- change class and configuration epoch, cohort exchangeability, mature failure definition, complete release inclusion, material regression threshold, rollback exposure limit, tenant priors, demand/value/loss perimeter, coherent scenarios, common platform state, support/evidence gates and tail appetite
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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 material ai configuration regression rollbackcapped" }
→ finds "forecast_ai_configuration_regression_loss"
gitrevio_capability_describe
{ "capability_id": "forecast_ai_configuration_regression_loss" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_ai_configuration_regression_loss", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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