Value architecture migration option

What it's for

Turns migrate-now-or-later into an auditable real option: wait only when information has measurable value, migrate only on evidence available at that date, and show the cost of rigid timing.

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.

{
  "minimum_option_value": 1,
  "scenarios": [
    {
      "current_architecture_costs_by_period": [
        10,
        10
      ],
      "id": "migration-pays",
      "migrated_architecture_costs_by_period": [
        2,
        2
      ],
      "migration_costs_by_period": [
        5,
        1
      ],
      "migration_downtime_loss_by_period": [
        0,
        0
      ],
      "probability": 0.5,
      "signal_path": [
        "unknown",
        "favorable"
      ]
    },
    {
      "current_architecture_costs_by_period": [
        10,
        10
      ],
      "id": "migration-does-not-pay",
      "migrated_architecture_costs_by_period": [
        12,
        12
      ],
      "migration_costs_by_period": [
        5,
        1
      ],
      "migration_downtime_loss_by_period": [
        0,
        0

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.

{
  "configuration": {
    "discount_rate_per_period": 0,
    "signal_history_is_refining_by_construction": true,
    "tail_probability": 0.5
  },
  "decision": "preserve_signal_contingent_migration_option",
  "failed_gates": [],
  "guardrails": [
    "The policy can use only the submitted signal history available by that period; future outcomes, post-migration evidence, and reconstructed hindsight labels cannot move backward in the tree.",
    "Current and migrated run cost, migration spend, downtime loss, horizon, discounting, feasibility and signal release require comparable architecture/finance counterfactuals and local out-of-time calibration.",
    "The option covers one irreversible migration and represented futures; parallel migration, rollback, technical failure, security approval, tax, capacity and vendor behavior require explicit modeling or separate stress tests.",
    "A supported option is not migration authorization and cannot be converted into a team, vendor, architect, or employee performance judgment."
  ],
  "method": "signal_tree_architecture_migration_optimal_stopping_v1",
  "policy_nodes": [
    {
      "action": "wait",
      "conditional_expected_cost_from_node": 16.5,
      "node_probability": 1,
      "period": 0,
      "signal_history": [
        "unknown"
      ]
    },
    {
      "action": "wait",
      "conditional_expected_cost_from_node": 10,
      "node_probability": 0.5,
      "period": 1,
      "signal_history": [
        "unknown",
        "adverse"
      ]
    },
    {
      "action": "migrate",
      "conditional_expected_cost_from_node": 3,
      "node_probability": 0.5,
      "period": 1,
      "signal_history": [
        "unknown",
        "favorable"
      ]

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 Construct coherent future scenarios containing a refining signal path and comparable current-run, migrated-run, migration, and downtime costs by period, with immutable signal release times.
2. 2 Work backward through the signal tree, forcing all scenarios with the same observed history to take the same wait-or-migrate action and valuing migration from the current period without double-counting sunk run costs.
3. 3 Replay the policy scenario by scenario, reconcile it to the dynamic program, compare it with never, every fixed date and perfect information, calculate CVaR, and apply governed savings, option-value, and tail-cost gates.

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": "value an irreversible architecture migration as" }
  → finds "value_architecture_migration_option"

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

gitrevio_capability_run
  { "capability_id": "value_architecture_migration_option", "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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