Optimize commercial resilience portfolio

What it's for

Moves from 'this component is risky' to the strongest defensible resilience investment portfolio while preventing overlapping contract exposure from manufacturing ROI.

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.

{
  "action_effects": [
    {
      "action_id": "harden-api",
      "component_id": "api",
      "evidence_verified": true,
      "id": "harden-api-effect",
      "residual_loss_multipliers": [
        0.2,
        0.2,
        0.2,
        0.2
      ]
    },
    {
      "action_id": "harden-database",
      "component_id": "database",
      "evidence_verified": true,
      "id": "harden-database-effect",
      "residual_loss_multipliers": [
        0.5,
        0.5,
        0.5,
        0.5
      ]
    }
  ],
  "budget": 25,
  "capacity_units": 3,
  "commercial_sources": [
    {
      "evidence_verified": true,
      "id": "enterprise-contract",
      "value_at_risk": 100
    },
    {
      "evidence_verified": true,
      "id": "usage-margin",
      "value_at_risk": 200
    }
  ],
  "components": [
    {
      "evidence_verified": true,

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.

{
  "action_diagnostics": [
    {
      "action_id": "harden-database",
      "capacity_units": 2,
      "cost": 20,
      "dependency_ids": [],
      "effect_count": 1,
      "exclusion_ids": [],
      "selected": true,
      "standalone_expected_avoided_loss": 35
    },
    {
      "action_id": "harden-api",
      "capacity_units": 1,
      "cost": 10,
      "dependency_ids": [],
      "effect_count": 1,
      "exclusion_ids": [],
      "selected": false,
      "standalone_expected_avoided_loss": 20
    }
  ],
  "assumptions": [
    "Actions apply prospectively validated residual-loss multipliers to explicit technical components; multiple actions and simultaneous failures combine multiplicatively, so overlapping standalone benefits cannot be added and no commercial source loses more than its governed value.",
    "Commercial values, action costs, scenarios, probability law, horizon and risk gates share one finance-owned perimeter. Dependencies, exclusions, budget and execution capacity describe genuinely feasible aggregate interventions.",
    "Exact mode proves the optimum only for the supplied finite model. Beam search is deterministic but uncertified; neither mode proves causal action effects, availability, legal obligations, liquidity sufficiency or authority to execute an architecture or procurement change.",
    "The portfolio concerns aggregate systems and commercial exposures, never employee evaluation, customer quality or blame. Accountable architecture, SRE, security, legal, finance and product owners approve any intervention."
  ],
  "constraints": {
    "budget": 25,
    "capacity_units": 3,
    "critical_loss_threshold": null,
    "maximum_critical_loss_probability": null,
    "maximum_residual_cvar_loss": null,
    "risk_aversion": 0,
    "tail_probability": 0.1
  },
  "decision": "commercial_resilience_portfolio_supported",
  "method": "unique_source_graph_commercial_resilience_portfolio_v1",
  "resilience_pareto_frontier": [
    {
      "expected_residual_loss": 100,
      "residual_cvar_loss": 300,

How it works

Network & dependency analysis — Trace how load, failure and knowledge propagate through a graph of teams, services or components.

1. 1 Freeze unique commercial loss sources, component exposure edges, joint failure scenarios, and executable actions with scenario-specific residual-loss multipliers, dependencies, exclusions, full cost and capacity.
2. 2 For every evaluated action set, multiply effects on each component, recompute source-level survival-union loss from the graph, and derive expected residual loss, CVaR and critical-loss probability without adding standalone benefits.
3. 3 Choose the feasible set maximizing expected avoided loss net of spend plus governed tail improvement, return the cost/expected-loss/CVaR Pareto frontier, and distinguish exact enumeration from uncertified deterministic beam search.

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 a budgeted capacityfeasible technical resilience" }
  → finds "optimize_commercial_resilience_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_commercial_resilience_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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