Optimize AI evaluation value of information portfolio
Select additional AI evaluation plans by multi-stratum posterior-predictive value of sample information, discounting duplicate content and optimizing budget, reviewer capacity, delay, quality lower bounds and expected/CVaR incremental false-deployment loss with exact or disclosed beam search.
What it's for
Shows CTOs which AI evaluations are worth buying now because they can change a deployment decision—not because they produce another benchmark number.
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 |
|---|---|---|---|
| beam_width | integer ≥ 1, ≤ 10000 | Numerical control | Optional |
| deployment_decisions | array of objects (9 fields) | Evidence | Yes |
| evaluation_budget | number ≥ 0 | Your calibration | Yes |
| evaluation_plans | array of objects (8 fields) | Evidence | Yes |
| evidence_strata | array of objects (7 fields) | Evidence | Yes |
| exact_enumeration_limit | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_cvar_incremental_false_deployment_loss | any | Your calibration | Optional |
| maximum_expected_incremental_false_deployment_loss | any | Your calibration | Optional |
| maximum_plan_lead_time_periods | integer ≥ 0, ≤ 1000 | Your calibration | Optional |
| minimum_effective_independence_fraction | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_quality_lower_bound_z | number ≥ 0, ≤ 10 | Your calibration | Optional |
| prior_failures | number > 0 | Your calibration | Optional |
| prior_successes | number > 0 | Your calibration | Optional |
| reviewer_capacity_hours | number ≥ 0 | Your calibration | Yes |
| risk_aversion | number ≥ 0 | Your calibration | Optional |
| scenarios | array of objects (6 fields) | Evidence | Yes |
| seed | integer ≥ 0 | Numerical control | Optional |
| simulations | integer ≥ 100, ≤ 100000 | Numerical control | Optional |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each deployment_decisions
record
| Field | Type | Required |
|---|---|---|
| delay_loss_per_period | number (≥ 0) | Yes |
| evidence_verified | boolean | Yes |
| forecast_request_count | integer (≥ 1) | Yes |
| high_impact | boolean | Yes |
| id | string (non-empty) | Yes |
| loss_per_failure | number (≥ 0) | Yes |
| minimum_quality_score | number (≥ 0, ≤ 1) | Yes |
| required_stratum_ids | array of string (≥ 1 item) | Yes |
| value_per_success | number (≥ 0) | Yes |
{
"deployment_decisions": [
{
"delay_loss_per_period": 10,
"evidence_verified": true,
"forecast_request_count": 10000,
"high_impact": true,
"id": "support-v2-deployment",
"loss_per_failure": 1,
"minimum_quality_score": 0.7,
"required_stratum_ids": [
"billing",
"technical"
],
"value_per_success": 1
}
],
"evaluation_budget": 100,
"evaluation_plans": [
{
"additional_case_counts": {
"billing": 40,
"technical": 40
},
"decision_id": "support-v2-deployment",
"effective_independence_fraction": 1,
"evidence_verified": true,
"fixed_cost": 20,
"id": "balanced-80-case-evaluation",
"lead_time_periods": 0,
"reviewer_hours": 8
}
],
"evidence_strata": [
{
"decision_id": "support-v2-deployment",
"deployment_weight": 0.8,
"evidence_verified": true,
"id": "support-billing",
"resolved_failure_count": 2,
"resolved_success_count": 8,
"stratum_id": "billing"
},
{Truncated for display — the full payload is 75 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": [
"Resolved evidence is prospective, exchangeable within each declared stratum and mature before the decision; deployment weights, success definitions and strata are stable and exhaustive for the horizon.",
"Evaluation plans add genuinely new cases at the stated effective independence fraction. Near-duplicate or contaminated cases must be discounted before sample counts enter the Beta-Binomial update.",
"Value, failure loss, delay loss, budget, reviewer capacity, horizon, quality thresholds, scenarios and tail appetite are locally owned and share one non-overlapping economic perimeter.",
"The selected plan is a value-of-information recommendation, not model-safety proof, deployment, procurement, privacy/security/legal approval, data-transfer authorization or an automatic production change."
],
"baseline_current_state": [
{
"decision_id": "support-v2-deployment",
"deploy_without_more_evaluation": false,
"expected_quality_by_stratum": {
"billing": 0.7727,
"technical": 0.7727
},
"quality_lower_bound_by_stratum": {
"billing": 0.5737,
"technical": 0.5737
}
}
],
"configuration": {
"decision_rule": "deploy_only_when_every_required_stratum_quality_lower_bound_clears_threshold_and_posterior_expected_value_is_positive",
"dependence_rule": "one_scenario_draw_shared_across_all_decisions_per_simulation",
"learning_rule": "posterior_predictive_beta_binomial_samples_discounted_by_effective_content_independence",
"prior_failures": 0.5,
"prior_successes": 0.5,
"risk_aversion": 0,
"seed": 31,
"simulations": 200
},
"constraints": {
"evaluation_budget": 100,
"maximum_cvar_incremental_false_deployment_loss": null,
"maximum_expected_incremental_false_deployment_loss": null,
"maximum_plan_lead_time_periods": 6,
"minimum_effective_independence_fraction": 0.8,
"minimum_quality_lower_bound_z": 1.645,
"reviewer_capacity_hours": 20,
"tail_probability": 0.1
},
"decision": "ai_evaluation_evsi_portfolio_supported",
"evaluation_pareto_frontier": [
{Truncated for display — the full payload is 113 lines.
How it works
Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.
- 1 Build tenant-local Beta posteriors for every required deployment stratum, deployment-weighted economics and the safe current deploy-or-hold baseline.
- 2 Discount each proposed plan's cases by independently audited content independence, simulate Beta-Binomial outcomes, and re-decide deployment only when every stratum lower bound and posterior expected value clear local gates.
- 3 Choose at most one plan per deployment under budget, review, lead-time and false-deployment expected/CVaR constraints; report plan-level EVSI, baseline, Pareto frontier and exact or heuristic solver certainty.
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
- The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
- Resolved outcomes are prospective and exchangeable within stable exhaustive strata, plan cases are genuinely new at the stated independence fraction, and deployment value/loss scenarios share one horizon and perimeter.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- EVSI values only the submitted plans and decision model; exactness covers the finite plan space. Selection is never safety proof, deployment, procurement, privacy/security/legal approval or data-transfer authorization.
Minimum evidence
- deployment_decisions: required and organization-defined
- evidence_strata: required and organization-defined
- evaluation_plans: required and organization-defined
- scenarios: required and organization-defined
- evaluation_budget: required and organization-defined
- reviewer_capacity_hours: required and organization-defined
How to validate it
Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.
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
- one versioned decision-evidence-plan projection joining only prospectively resolved outcomes and contamination-audited case plans, preserving shared scenarios, no-test baselines and effective rather than nominal sample counts
- stratum exhaustiveness/exchangeability and weights, success maturity, Beta priors, quality lower-bound rule, content-independence discount, plan executability, budget/reviewer/lead-time capacity, deployment value/failure/delay loss, expected/CVaR appetite, solver boundary and production approval
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": "select additional ai evaluation plans by" }
→ finds "optimize_ai_evaluation_value_of_information_portfolio"
gitrevio_capability_describe
{ "capability_id": "optimize_ai_evaluation_value_of_information_portfolio" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "optimize_ai_evaluation_value_of_information_portfolio", "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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