Optimize board technology attention portfolio

What it's for

Gives boards and investors a defensible attention frontier: which technology decisions deserve scarce agenda and assurance capacity when direct and common downside are modeled together.

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.

{
  "agenda_time_minutes": 60,
  "assurance_budget": 100000,
  "attention_options": [
    {
      "agenda_time_minutes": 20,
      "attention_type_id": "independent-review",
      "available_scenario_ids": [
        "base",
        "stress"
      ],
      "common_failure_detection_probability_scenarios": [
        0.8,
        0.7
      ],
      "correction_success_probability_scenarios": [
        0.9,
        0.8
      ],
      "decision_id": "platform-migration",
      "dependency_option_ids": [],
      "evidence_verified": true,
      "exclusion_option_ids": [],
      "failure_detection_probability_scenarios": [
        0.9,
        0.8
      ],
      "id": "platform-migration-review",
      "implementation_cost": 20000,
      "operating_cost_scenarios": [
        5000,
        10000
      ],
      "resource_demands": {
        "assurance-hours": 1
      }
    },
    {
      "agenda_time_minutes": 20,
      "attention_type_id": "independent-review",
      "available_scenario_ids": [
        "base",
        "stress"
      ],

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": [
    "Decision owners provide a materially complete strategic decision and shared-risk perimeter; option detection and correction effects are prospectively validated and scenario-aligned before optimization.",
    "Attention effects compound through residual failure survival, common group loss is counted once, and exact optimality is claimed only when every declared subset is evaluated."
  ],
  "baseline_no_attention": {
    "conditional_value_at_risk": 1584000,
    "expected_loss": 175300,
    "expected_net_value": 570150,
    "residual_failure_probabilities": {
      "ai-rollout": 0.2125,
      "platform-migration": 0.2156
    }
  },
  "constraints": {
    "agenda_time_minutes": 60,
    "assurance_budget": 100000,
    "resource_capacities": {
      "assurance-hours": 2
    },
    "risk_aversion": 0.25,
    "tail_probability": 0.05
  },
  "decision": "review_risk_adjusted_board_attention_portfolio",
  "failed_gates": [
    {
      "gate": "mandatory_attention_type_missing",
      "rejected_state_count": 3
    }
  ],
  "limitations": [
    "The optimizer allocates a submitted board-attention portfolio; it does not determine fiduciary duties, materiality under law, disclosure obligations, board judgment, executive performance or authority to act.",
    "Unrepresented strategic interactions, endogenous management behavior, option-effect uncertainty and losses outside the submitted scenarios can make the frontier incomplete."
  ],
  "method": "beta_binomial_common_risk_cvar_board_attention_portfolio_v1",
  "portfolio_pareto_frontier": [
    {
      "agenda_time_minutes": 40,
      "conditional_value_at_risk": 843400,
      "expected_net_value": 807850,
      "selected_option_ids": [
        "ai-rollout-review",
        "platform-migration-review"
      ]

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 coherent scenarios, decision-specific Beta failure uncertainty and one common event per strategic risk group; count shared group loss once rather than once per decision.
2. 2 Compound each prospectively validated attention option through failure detection and correction, then enforce mandatory attention types, scenario availability, dependencies, exclusions, agenda time, assurance budget, shared resources and residual-risk ceilings.
3. 3 Rank feasible portfolios by expected net strategic value minus downside CVaR, return a value-risk-time Pareto frontier and certify global optimality only after exhaustive subset enumeration.

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 board technologyattention portfolio under" }
  → finds "optimize_board_technology_attention_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_board_technology_attention_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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