Optimize AI configuration rollout portfolio

What it's for

Creates an AI release control tower: which prompt/model/tool-policy changes can ship together, at what staged exposure, without double-counting shared blast radius or exhausting rollback and review capacity.

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.

{
  "applications": [
    {
      "evidence_verified": true,
      "id": "support",
      "maximum_concurrent_changes": 1,
      "value_at_risk_scenarios": [
        100000,
        200000
      ]
    }
  ],
  "implementation_budget": 10000,
  "releases": [
    {
      "affected_application_ids": [
        "support"
      ],
      "evidence_verified": true,
      "id": "support-v3",
      "loss_per_failure_scenarios": [
        100,
        200
      ],
      "maximum_failure_probability": 0.1,
      "request_volume_scenarios": [
        10000,
        12000
      ],
      "required_control_ids": [
        "rollback"
      ],
      "value_per_success_scenarios": [
        2,
        2
      ]
    }
  ],
  "resource_capacities": [
    {
      "capacity_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.

{
  "application_diagnostics": [
    {
      "application_id": "support",
      "maximum_concurrent_changes": 1,
      "selected_loss_probability_by_scenario": [
        0,
        0
      ]
    }
  ],
  "assumptions": [
    "Each plan represents the exact evaluated configuration and a prospectively executable traffic schedule, detection/rollback system, full cost and resource load under one coherent scenario order.",
    "Direct failure loss is release-specific; shared application value at risk is counted once from the joint probability that any touching release leaves residual harm, including common platform regression.",
    "Controls, failure ceilings, scenario availability, application concurrency, dependencies, exclusions, budget and shared resources are hard gates before expected value and CVaR regret are optimized.",
    "Selection is not deployment, safety/privacy/security/legal approval, an SLA or a judgment about a prompt author, employee, team, model provider or country."
  ],
  "baseline_current_state": {
    "expected_net_value": 0,
    "expected_unique_application_loss": 0,
    "plan_ids": [
      "hold"
    ]
  },
  "constraints": {
    "implementation_budget": 10000,
    "maximum_cvar_economic_regret": null,
    "risk_aversion": 0.25,
    "tail_probability": 0.05
  },
  "decision": "ai_configuration_rollout_portfolio_supported",
  "failed_gates": [],
  "method": "ai_configuration_staged_rollout_unique_blast_radius_cvar_v1",
  "portfolio_pareto_frontier": [
    {
      "economic_regret_cvar": 5792.215,
      "expected_net_value": 0,
      "implementation_cost": 0
    }
  ],
  "release_diagnostics": [
    {
      "average_traffic_fraction": 0,
      "peak_traffic_fraction": 0,

How it works

Causal inference & experiment design — Separate what a change caused from what merely moved alongside it.

1. 1 Translate every staged traffic plan, scenario failure probability and tested detection/rollback rate into direct successful exposure and residual failure loss.
2. 2 For each shared application, combine residual risks through joint survival and add common regression before pricing unique value at risk once; enforce concurrent-change limits.
3. 3 Reject plans missing controls, failure support or scenario availability, enforce relations/budget/resources, and solve the multiple-choice portfolio exactly or by disclosed beam using expected value and CVaR regret.

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": "select one current or staged rollout" }
  → finds "optimize_ai_configuration_rollout_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_ai_configuration_rollout_portfolio", "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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