Optimize selective human AI review policy

What it's for

Routes scarce expert judgment to the decisions where validated human–AI collaboration creates the most value, while showing leaders the cost, capacity overload probability and residual tail risk.

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.

{
  "maximum_expected_cost": 30,
  "policy_options": [
    {
      "complementarity_supported": false,
      "eligible": true,
      "fixed_cost": 0,
      "id": "high-auto",
      "residual_loss_scenarios": [
        100,
        100
      ],
      "review_hours_scenarios": [
        0,
        0
      ],
      "segment_id": "high-risk",
      "uses_human_review": false,
      "variable_cost_scenarios": [
        0,
        0
      ]
    },
    {
      "complementarity_supported": true,
      "eligible": true,
      "fixed_cost": 5,
      "id": "high-review",
      "residual_loss_scenarios": [
        20,
        20
      ],
      "review_hours_scenarios": [
        5,
        5
      ],
      "segment_id": "high-risk",
      "uses_human_review": true,
      "variable_cost_scenarios": [
        10,
        10
      ]
    },
    {

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.

{
  "decision": "activate_selective_human_ai_review_policy",
  "excluded_policy_options": [],
  "failed_gates": [],
  "guardrails": [
    "Every human-review option must inherit version-matched complementarity evidence; ineligible options are excluded rather than made attractive through assumed benefits.",
    "Scenario columns must preserve joint demand, loss, cost and review-capacity shocks. Average review hours alone cannot establish feasibility; capacity-breach probability and residual-loss tail risk remain explicit.",
    "Exactness applies only inside the represented option set and scenarios. Beam mode has no global certificate, and the selected routing policy never removes accountable human ownership or authorizes person-level performance scoring."
  ],
  "method": "coherent_scenario_multichoice_selective_human_ai_review_v1",
  "selected_policy_by_segment": [
    {
      "policy_option_id": "high-review",
      "segment_id": "high-risk",
      "uses_human_review": true
    },
    {
      "policy_option_id": "medium-review",
      "segment_id": "medium-risk",
      "uses_human_review": true
    }
  ],
  "solver": {
    "beam_width": null,
    "candidate_state_count": 4,
    "evaluated_states": 4,
    "global_optimality_certificate": true,
    "mode": "exact_multichoice_enumeration"
  },
  "summary": {
    "baseline_expected_loss": 180,
    "expected_cost": 30,
    "expected_net_avoided_loss": 100,
    "expected_residual_loss": 50,
    "expected_review_hours": 10,
    "human_review_segment_count": 2,
    "review_capacity_breach_probability": 0,
    "segment_count": 2,
    "selected_policy_option_ids": [
      "high-review",
      "medium-review"
    ],
    "tail_cvar_residual_loss": 50
  },

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 decision segments and coherent joint scenarios for baseline loss and review-hour capacity; enumerate segment-specific automation and review options with residual loss, full variable/fixed cost and scenario workload.
2. 2 Exclude locally ineligible options and every human-review option lacking version-matched complementarity evidence, then select exactly one remaining policy per segment.
3. 3 Maximize expected net avoided loss subject to expected cost, scenario review-capacity breach probability and optional residual-loss CVaR; enumerate all multi-choice states inside the exact boundary and disclose feasible beam search outside it.

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": "choose one eligible automation or humanreview" }
  → finds "optimize_selective_human_ai_review_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_selective_human_ai_review_policy", "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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