Optimize multi period calibration maintenance
Optimize a finite-horizon analytics maintenance schedule by propagating each function's healthy/degraded Markov belief under passive operation or recalibration, valuing healthy decisions and uncalibrated loss, enforcing period cash and specialist-capacity constraints, and disclosing exact state enumeration versus deterministic beam search.
What it's for
Allocates scarce analytics-engineering capacity across the functions whose recalibration creates the most risk-adjusted decision value over time—not merely the oldest models first.
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.
| Field | Type | Role | Required |
|---|---|---|---|
| beam_width | integer ≥ 10, ≤ 100000 | Numerical control | Optional |
| budget_by_period | array of number ≥ 1 item | Evidence | Yes |
| capacity_by_period | array of number ≥ 1 item | Evidence | Yes |
| discount_rate_per_period | number ≥ 0, ≤ 10 | Your calibration | Optional |
| functions | array of objects (9 fields) ≥ 1 item | Evidence | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_exact_states | integer ≥ 2, ≤ 2000000 | Numerical control | Optional |
Each functions
record
| Field | Type | Required |
|---|---|---|
| healthy_value_by_period | array of number (≥ 1 item) | Yes |
| id | string (non-empty) | Yes |
| initial_healthy_probability | number (≥ 0, ≤ 1) | Yes |
| passive_healthy_retention_probability | number (≥ 0, ≤ 1) | Yes |
| passive_recovery_probability | number (≥ 0, ≤ 1) | Yes |
| recalibration_capacity_units | number (≥ 0) | Yes |
| recalibration_cost_by_period | array of number (≥ 1 item) | Yes |
| recalibration_success_probability | number (≥ 0, ≤ 1) | Yes |
| uncalibrated_loss_by_period | array of number (≥ 1 item) | Yes |
{
"budget_by_period": [
5,
0
],
"capacity_by_period": [
1,
0
],
"functions": [
{
"healthy_value_by_period": [
100,
100
],
"id": "completion-forecast",
"initial_healthy_probability": 0.2,
"passive_healthy_retention_probability": 1,
"passive_recovery_probability": 0,
"recalibration_capacity_units": 1,
"recalibration_cost_by_period": [
5,
5
],
"recalibration_success_probability": 1,
"uncalibrated_loss_by_period": [
100,
100
]
},
{
"healthy_value_by_period": [
10,
10
],
"id": "decision-policy",
"initial_healthy_probability": 0.2,
"passive_healthy_retention_probability": 1,
"passive_recovery_probability": 0,
"recalibration_capacity_units": 1,
"recalibration_cost_by_period": [
5,
5
],Truncated for display — the full payload is 52 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": "run_calibration_maintenance_plan",
"guardrails": [
"Transition and recalibration-success probabilities must be estimated from prospective local maintenance episodes and revalidated by function family and regime; a Markov approximation does not erase hidden state or common-cause drift.",
"Healthy value, uncalibrated loss, complete recalibration cost, budgets, capacity and discounting are organization-owned. The plan optimizes aggregate functions, never people, and does not authorize unsafe activation.",
"Exact mode certifies only the represented finite-horizon open-loop model. Beam mode is a disclosed feasible heuristic; neither protects against omitted dependencies, correlated failures, implementation delays or unrepresented scenarios."
],
"method": "multi_period_markov_belief_calibration_maintenance_v1",
"schedule": [
{
"period": 0,
"recalibrate_function_ids": [
"completion-forecast"
],
"recalibration_capacity_units": 1,
"recalibration_cost": 5
},
{
"period": 1,
"recalibrate_function_ids": [],
"recalibration_capacity_units": 0,
"recalibration_cost": 0
}
],
"solver": {
"beam_width": null,
"estimated_unpruned_state_count": 20,
"evaluated_transitions": 6,
"global_optimality_certificate": true,
"mode": "exact_state_enumeration"
},
"summary": {
"expected_avoided_uncalibrated_loss": 160,
"expected_discounted_net_value": 183,
"expected_uncalibrated_loss": 16,
"function_count": 2,
"incremental_expected_discounted_value": 315,
"no_maintenance_expected_discounted_net_value": -132,
"no_maintenance_expected_uncalibrated_loss": 176,
"planning_period_count": 2,
"recalibrated_function_count": 1,
"recalibrated_function_ids": [
"completion-forecast"
],Truncated for display — the full payload is 50 lines.
How it works
Markov & state-space control — Choose a policy over evolving states when today's action changes tomorrow's options.
- 1 Freeze each function's current healthy belief, passive retention/recovery and recalibration-success probabilities, period healthy value, degraded loss, complete maintenance cost and specialist capacity demand.
- 2 At each period enumerate feasible recalibration subsets inside the exact boundary—or deterministic value/cost/capacity candidates outside it—then propagate post-action Markov health beliefs and discounted net value.
- 3 Select the best complete schedule, compare it with no maintenance, reconcile avoided uncalibrated loss and cost, and expose first-period action, entire plan and solver certainty.
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
- States are decision-sufficient at the chosen cadence and transition evidence is recent, identifiable, and not silently pooled across incompatible regimes.
- Local transitions are prospective and sufficiently Markov at the chosen period; recalibration effects and delays are valid; function value/loss is nonduplicative; budgets and specialist capacity are truly fungible; omitted dependencies and common-cause drift are immaterial.
- A policy is conditional on the declared state abstraction and transition model; hidden omitted state can invalidate its ranking.
- Exactness covers only the represented open-loop model; beam mode has no global certificate; function maintenance never becomes employee scheduling, and the output does not authorize unsafe activation or suppress required governance.
Minimum evidence
- functions: at least 1 rows/items
- budget_by_period: at least 1 rows/items
- capacity_by_period: at least 1 rows/items
How to validate it
Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.
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
- prospectively estimated function-family maintenance transition panel joined to nonduplicative decision economics, realized recalibration outcomes, specialist effort, implementation delay and approved multi-period plan
- health state and Markov period, transition/maintenance-effect estimation, value/loss/cost/currency/horizon, budgets, fungible capacity, discounting, omitted dependencies/common shocks, exact state boundary, beam width and execution 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 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 a finitehorizon analytics maintenance schedule" }
→ finds "optimize_multi_period_calibration_maintenance"
gitrevio_capability_describe
{ "capability_id": "optimize_multi_period_calibration_maintenance" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "optimize_multi_period_calibration_maintenance", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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