Optimize decision authority queue policy
Optimize delegation and escalation by assigning one eligible authority option to each decision class while internalizing nonlinear Erlang-C waiting externalities across shared reviewer pools, wrong-decision and escalation loss, coherent demand scenarios, operating cost, utilization-breach probability and CVaR.
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
Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| authority_options | array of objects (11 fields) ≥ 1 item | Evidence | Yes |
| beam_width | integer ≥ 10, ≤ 100000 | Numerical control | Optional |
| decision_classes | array of objects (5 fields) ≥ 1 item | Evidence | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_cvar_total_loss | number ≥ 0 | Your calibration | Optional |
| maximum_exact_states | integer ≥ 2, ≤ 2000000 | Numerical control | Optional |
| maximum_expected_operating_cost | number ≥ 0 | Your calibration | Yes |
| maximum_pool_utilization | number > 0, < 1 | Your calibration | Optional |
| maximum_probability_utilization_breach | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_expected_net_avoided_loss | number | Your calibration | Optional |
| planning_horizon_hours | number > 0 | Your calibration | Yes |
| reviewer_pools | array of objects (2 fields) ≥ 1 item | Evidence | Yes |
| scenarios | array of objects (2 fields) | Evidence | Yes |
| tail_probability | number > 0, ≤ 0.5 | Your calibration | Optional |
Each authority_options
record
| Field | Type | Required |
|---|---|---|
| authority_level | string (non-empty) | Yes |
| decision_class_id | string (non-empty) | Yes |
| eligible | boolean | Yes |
| escalation_delay_hours_scenarios | array of number (≥ 2 items) | Yes |
| escalation_probability_scenarios | array of number (≥ 2 items) | Yes |
| fixed_cost | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| reviewer_pool_id | string (non-empty) | Yes |
| service_hours_per_decision_scenarios | array of number (≥ 2 items) | Yes |
| variable_cost_per_decision_scenarios | array of number (≥ 2 items) | Yes |
| wrong_decision_probability_scenarios | array of number (≥ 2 items) | Yes |
{
"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",Truncated for display — the full payload is 179 lines.
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,Truncated for display — the full payload is 66 lines.
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 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 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 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.
Assumptions & guardrails
- Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
- Classes are mutually exclusive; scenario columns preserve joint demand/risk/cost shocks; pools are fungible and stationary; arrivals approximate Poisson and pooled service exponential at the mean; service/error/escalation effects are prospective; mandatory authority floors remain eligible constraints.
- The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
- This optimizes aggregate authority design, never named managers. Erlang-C is an approximation requiring simulation when priority, abandonment, schedules or heavy tails matter; no output can bypass legal, security or fiduciary approval.
Minimum evidence
- decision_classes: at least 1 rows/items
- authority_options: at least 1 rows/items
- reviewer_pools: at least 1 rows/items
- scenarios: required and organization-defined
- planning_horizon_hours: required and organization-defined
- maximum_expected_operating_cost: required and organization-defined
How to validate it
Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- prospective authority-policy planning projection joining mutually exclusive decision demand, fungible reviewer capacity, validated option effects, complete costs, mandatory authority floors and coherent common-shock scenarios
- class perimeter, option eligibility and mandatory floors, pool fungibility, scenario law, queue approximation, planning horizon, cost/utilization/tail limits, solver boundary and implementation authority
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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 20 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 aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.