Optimize agentic autonomy portfolio

What it's for

Lets leadership expand AI-agent autonomy only where tested controls, reviewer capacity, economics and downside risk support it—while preserving human approval for irreversible actions.

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.

{
  "action_classes": [
    {
      "asset_id": "payments",
      "asset_loss_fraction_if_failure": 0.5,
      "critical": false,
      "evidence_verified": true,
      "external_side_effects": true,
      "id": "deploy",
      "irreversible": false
    },
    {
      "asset_id": "payments",
      "asset_loss_fraction_if_failure": 0.8,
      "critical": true,
      "evidence_verified": true,
      "external_side_effects": true,
      "id": "migration",
      "irreversible": true
    }
  ],
  "autonomy_modes": [
    {
      "action_class_id": "deploy",
      "action_volume_scenarios": [
        10,
        12
      ],
      "authorized": true,
      "available_scenario_ids": [
        "base",
        "stress"
      ],
      "compute_cost_per_action_scenarios": [
        0.1,
        0.1
      ],
      "dependency_mode_ids": [],
      "direct_loss_per_failure_scenarios": [
        5,
        8
      ],
      "evidence_verified": true,
      "exclusion_mode_ids": [],

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.

{
  "action_class_diagnostics": [
    {
      "action_class_id": "deploy",
      "asset_id": "payments",
      "critical": false,
      "external_side_effects": true,
      "irreversible": false,
      "selected_mode": "bounded_autonomous",
      "selected_mode_id": "deploy-bounded"
    },
    {
      "action_class_id": "migration",
      "asset_id": "payments",
      "critical": true,
      "external_side_effects": true,
      "irreversible": true,
      "selected_mode": "approval_required",
      "selected_mode_id": "migration-approval"
    }
  ],
  "assumptions": [
    "Each action class selects exactly one executable mode on common scenarios. Volumes, success, terminal loss, value, direct loss, compute, latency and review definitions share one horizon and are prospectively evidenced.",
    "Several action classes can reach one business asset; per-action failure unions within a mode and loss unions across modes, so governed asset value is not duplicated. Direct response loss, operating cost and business loss remain non-overlapping.",
    "Operational safety is a hard feasibility gate, not a price: authorization is mandatory, irreversible actions retain human approval, and external autonomous actions require sandbox, tested kill switch and rollback or approval.",
    "Exactness covers only the supplied finite scenario model; beam output is heuristic. No mode selection grants privileges, bypasses security/legal/fiduciary approval, evaluates named people or authorizes an external side effect or production change."
  ],
  "autonomy_pareto_frontier": [
    {
      "conditional_value_at_risk": 41.173,
      "expected_net_value": 111.3629,
      "implementation_spend": 4,
      "mode_ids": [
        "deploy-bounded",
        "migration-approval"
      ],
      "review_overload_probability": 0
    }
  ],
  "baseline_current_state": {
    "conditional_value_at_risk": 37.173,
    "expected_net_value": 46.1329,
    "expected_total_loss": 23.0971,
    "feasible": false,

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 unique assets, aggregate action classes and common scenarios; require one current and several prospectively evidenced executable modes with complete value, failure, direct-loss, cost, review, availability and relation vectors.
2. 2 Treat authorization and irreversible-action approval as hard constraints; require sandbox, tested kill switch and rollback or approval for external autonomous modes before pricing their economics.
3. 3 Enumerate the multiple-choice policy exactly when tractable or disclose value-ordered beam search, union repeated asset exposure, and return the expected-net-value/CVaR/implementation-spend Pareto frontier.

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": "choose one manual approvalrequired boundedautonomous or" }
  → finds "optimize_agentic_autonomy_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_agentic_autonomy_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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