Optimize decision authority queue policy

What it's for

Finds where decisions should be delegated or escalated by pricing both approval bottlenecks and wrong-decision risk, instead of optimizing speed and control in isolation.

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.

{
  "authority_options": [
    {
      "authority_level": "team",
      "decision_class_id": "architecture",
      "eligible": true,
      "escalation_delay_hours_scenarios": [
        0,
        0
      ],
      "escalation_probability_scenarios": [
        0,
        0
      ],
      "fixed_cost": 0,
      "id": "architecture-team",
      "reviewer_pool_id": "team",
      "service_hours_per_decision_scenarios": [
        1,
        1
      ],
      "variable_cost_per_decision_scenarios": [
        1,
        1
      ],
      "wrong_decision_probability_scenarios": [
        0.1,
        0.1
      ]
    },
    {
      "authority_level": "executive",
      "decision_class_id": "architecture",
      "eligible": true,
      "escalation_delay_hours_scenarios": [
        0,
        0
      ],
      "escalation_probability_scenarios": [
        0,
        0
      ],
      "fixed_cost": 0,
      "id": "architecture-executive",

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_decision_authority_policy",
  "excluded_authority_options": [],
  "failed_gates": [],
  "guardrails": [
    "Authority options govern decision classes and reviewer pools, never named employees. Eligibility, wrong-decision loss, escalation behavior, service demand and costs are local policy inputs requiring prospective validation.",
    "Erlang-C treats each represented pool as stationary with pooled exponential service at the scenario mean; use a queueing simulation when arrivals, priorities, abandonment, schedules or service tails violate that approximation.",
    "Scenario columns must preserve joint demand, risk and cost shocks. Exactness covers only represented options and futures; beam mode has no global certificate and cannot override mandatory legal, security or fiduciary approvals."
  ],
  "method": "coherent_scenario_erlang_c_decision_authority_optimization_v1",
  "reviewer_pool_diagnostics": [
    {
      "expected_utilization": 0.2,
      "maximum_scenario_utilization": 0.2,
      "reviewer_count": 1,
      "reviewer_pool_id": "executive",
      "utilization_breach_probability": 0
    },
    {
      "expected_utilization": 0.1,
      "maximum_scenario_utilization": 0.1,
      "reviewer_count": 1,
      "reviewer_pool_id": "team",
      "utilization_breach_probability": 0
    }
  ],
  "selected_authority_by_class": [
    {
      "authority_level": "team",
      "authority_option_id": "architecture-team",
      "decision_class_id": "architecture",
      "reviewer_pool_id": "team"
    },
    {
      "authority_level": "executive",
      "authority_option_id": "vendor-executive",
      "decision_class_id": "vendor",
      "reviewer_pool_id": "executive"
    }
  ],
  "solver": {
    "beam_width": null,
    "candidate_state_count": 4,
    "evaluated_states": 4,

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 mutually exclusive decision classes, coherent scenario arrivals/delay/risk/baseline loss, aggregate reviewer pools, and locally eligible authority options with service demand, error/escalation behavior and complete cost.
2. 2 For each candidate assignment aggregate class arrival and workload by reviewer pool and scenario, solve pooled Erlang-C waiting time, then price queue delay, escalation delay, wrong-decision loss, variable and fixed operating cost.
3. 3 Choose the highest expected net avoided loss inside cost, utilization-breach and optional CVaR constraints using exact multi-choice enumeration inside the state boundary or disclosed queue-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": "optimize delegation and escalation by assigning" }
  → finds "optimize_decision_authority_queue_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_decision_authority_queue_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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