Optimize service continuity investment portfolio

What it's for

Builds an investable resilience frontier across prevention, recovery and data-loss objectives while counting shared-dependency exposure once and certifying optimization only when exhaustive.

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.

{
  "continuity_options": [
    {
      "available_scenario_ids": [
        "base",
        "stress"
      ],
      "common_loss_reduction_fraction_scenarios": [
        0.05,
        0.05
      ],
      "data_loss_reduction_fraction_scenarios": [
        0.1,
        0.1
      ],
      "dependency_option_ids": [],
      "evidence_verified": true,
      "exclusion_option_ids": [],
      "id": "payments-current",
      "implementation_cost": 0,
      "is_current_state": true,
      "operating_cost_scenarios": [
        5,
        10
      ],
      "option_type": "current",
      "outage_prevention_probability_scenarios": [
        0.05,
        0.05
      ],
      "production_exercise_verified": true,
      "recovery_time_reduction_fraction_scenarios": [
        0.1,
        0.1
      ],
      "resource_demands": {},
      "satisfied_control_ids": [
        "tested-failover"
      ],
      "unit_id": "payments"
    },
    {
      "available_scenario_ids": [
        "base",

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": [
    "Risk units and shared dependencies are complete and value/loss is unique; outage priors and option prevention, recovery, data-loss and common-loss effects are locally calibrated from prospective production-representative exercises.",
    "Every selected option is executable in every scenario, satisfies required controls and relations, and uses complete implementation/operating cost plus shared capacity; common dependency loss is counted once."
  ],
  "baseline_current_state": {
    "conditional_value_at_risk": 1218010,
    "expected_financial_loss": 97965,
    "expected_net_value": 299432.765,
    "selected_option_ids": [
      "payments-current"
    ]
  },
  "constraints": {
    "investment_budget": 100,
    "resource_capacities": {
      "resilience-hours": 1
    },
    "risk_aversion": 0.25,
    "tail_probability": 0.05
  },
  "decision": "review_governed_service_continuity_investment_portfolio",
  "failed_gates": [
    {
      "gate": "maximum_expected_data_loss_exceeded",
      "rejected_state_count": 1
    },
    {
      "gate": "maximum_expected_recovery_time_exceeded",
      "rejected_state_count": 1
    },
    {
      "gate": "maximum_residual_outage_probability_exceeded",
      "rejected_state_count": 1
    }
  ],
  "limitations": [
    "The optimizer selects among preauthorized continuity investments; it does not guarantee uptime, recovery, contract compliance, customer retention, vendor performance or incident-free operation.",
    "No output authorizes production failover, architecture change, procurement, customer communication or contractual waiver; accountable owners must review feasibility and rehearse execution."
  ],
  "method": "beta_binomial_common_dependency_rto_rpo_cvar_portfolio_v1",
  "portfolio_pareto_frontier": [
    {
      "conditional_value_at_risk": 485429.2,

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 Draw service-unit Beta-binomial outage risk and coherent scenarios, then propagate option-specific prevention, recovery-time, data-loss and shared-dependency loss reduction through business value and loss.
2. 2 Reject portfolios violating production-exercise evidence, allowed option types, required controls, RTO/RPO/residual-risk ceilings, scenario availability, relations, budget or shared capacity.
3. 3 Enumerate the complete multiple-choice space when tractable and use a deterministic continuity-value beam otherwise; compare current state, selected policy, CVaR and the non-dominated value-risk frontier.

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 productionexercised continuity posture per" }
  → finds "optimize_service_continuity_investment_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_service_continuity_investment_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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