Audit agentic action control integrity

Audit operational AI-agent actions from bounded least-privilege permission scope through independently tested authorization, approval, sandbox, monitoring, rollback or compensation, and kill-switch controls, counting unique value exposure once.

Statistical audit & measurement Audit CTOs Tech CEOs IT managers Team leads Investors

What it's for

Gives leaders an evidence-backed inventory of which agentic tool actions are genuinely bounded, tested and recoverable before autonomy is expanded.

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
action_classes	array of objects (8 fields)	Evidence	Yes
as_of_period	integer ≥ 0	Your calibration	Yes
control_evidence	array of objects (7 fields)	Evidence	Yes
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_control_test_age_periods	integer ≥ 0, ≤ 10000	Your calibration	Optional
maximum_uncontrolled_high_impact_fraction	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_evidence_coverage	number ≥ 0, ≤ 1	Your calibration	Optional
permission_bindings	array of objects (8 fields)	Evidence	Yes

Each `action_classes` record

Field	Type	Required
evidence_verified	boolean	Yes
external_side_effects	boolean	Yes
id	string (non-empty)	Yes
impact_tier	one of "low", "moderate", "high", "critical"	Yes
required_control_kinds	array of values	Yes
reversible	boolean	Yes
tool_id	string (non-empty)	Yes
value_at_risk	number (> 0)	Yes

Example input

{
  "action_classes": [
    {
      "evidence_verified": true,
      "external_side_effects": true,
      "id": "production-deploy",
      "impact_tier": "critical",
      "required_control_kinds": [
        "sandbox"
      ],
      "reversible": true,
      "tool_id": "deployment-api",
      "value_at_risk": 100
    }
  ],
  "as_of_period": 10,
  "control_evidence": [
    {
      "action_class_id": "production-deploy",
      "control_kind": "authorization",
      "evidence_verified": true,
      "id": "deploy-authorization",
      "independently_tested": true,
      "last_test_period": 10,
      "test_passed": true
    },
    {
      "action_class_id": "production-deploy",
      "control_kind": "monitoring",
      "evidence_verified": true,
      "id": "deploy-monitoring",
      "independently_tested": true,
      "last_test_period": 10,
      "test_passed": true
    },
    {
      "action_class_id": "production-deploy",
      "control_kind": "approval",
      "evidence_verified": true,
      "id": "deploy-approval",
      "independently_tested": true,
      "last_test_period": 10,
      "test_passed": true
    },

Truncated for display — the full payload is 85 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.

Example output

{
  "action_class_diagnostics": [
    {
      "action_class_id": "production-deploy",
      "control_evidence_count": 6,
      "control_integrity_supported": true,
      "external_side_effects": true,
      "failed_gates": [],
      "impact_tier": "critical",
      "missing_control_kinds": [],
      "permission_binding_count": 1,
      "required_control_kinds": [
        "approval",
        "authorization",
        "kill_switch",
        "monitoring",
        "rollback",
        "sandbox"
      ],
      "reversible": true,
      "tool_id": "deployment-api",
      "valid_control_kinds": [
        "approval",
        "authorization",
        "kill_switch",
        "monitoring",
        "rollback",
        "sandbox"
      ],
      "value_at_risk": 100
    }
  ],
  "assumptions": [
    "Action classes and tool IDs represent aggregate operational capabilities, not people. Permission scope, impact, reversibility and business value share one point-in-time control-plane version.",
    "A control counts only when its independent test is current, passed and evidence verified. Policy text, framework mapping, a configured checkbox or an LLM assertion is not test evidence.",
    "High-impact control gaps expose governed value once per action class; they do not prove an incident, malicious intent, employee fault or model misconduct.",
    "The audit never authorizes deployment, privilege expansion, surveillance, investigation, disclosure, employment action or an external side effect."
  ],
  "configuration": {
    "as_of_period": 10,
    "mandatory_control_rule": "authorization_and_monitoring_plus_approval_for_external_or_high_impact_plus_rollback_or_compensation_plus_kill_switch_for_critical",
    "maximum_control_test_age_periods": 1,
    "maximum_uncontrolled_high_impact_fraction": 0,
    "minimum_evidence_coverage": 0.95,

Truncated for display — the full payload is 62 lines.

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1 Freeze aggregate action classes, tool IDs, impact/reversibility, permission bindings and independently tested control evidence at one control-plane period.
2 Derive mandatory controls from external side effects, impact and reversibility; reconcile exactly one bounded least-privilege binding plus current passed evidence for every required control.
3 Retain duplicate claims and bindings, gate high-impact coverage and evidence completeness, and union each action class's governed value rather than multiplying it by missing controls.

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

Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
Action classes are versioned aggregate capabilities rather than people, and permissions, test evidence, business value, impact and reversibility describe the same deployed control-plane version.
Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
A control gap is not proof of an incident, malicious model or employee intent, and never authorizes deployment, privilege expansion, surveillance, investigation, disclosure, employment action or an external side effect.

Minimum evidence

action_classes: required and organization-defined
permission_bindings: required and organization-defined
control_evidence: required and organization-defined
as_of_period: required and organization-defined

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

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

one point-in-time agent control-plane projection joining deployed action/tool versions to effective permission scope and immutable independent control-test results without inferring controls from policy text or configuration presence
action/tool taxonomy and version, scope and least-privilege semantics, external-impact and reversibility classification, unique value/currency/horizon, control requirements, test independence/freshness/pass meaning, evidence and high-impact coverage gates, pseudonymization and security/platform/legal/risk/finance ownership

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": "audit operational aiagent actions from bounded" }
  → finds "audit_agentic_action_control_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_agentic_action_control_integrity", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

Forecast agentic action loss

Forecast expected and tail operational AI-agent loss with tenant-local empirical-Bayes absorbing Markov chains across execution, deviation, containment, recovery, completion and loss, preserving shared control-failure regimes and unresolved-chain mass.

Markov & state-space control

Optimize agentic autonomy portfolio

Choose one manual, approval-required, bounded-autonomous or autonomous operating mode per action class, maximizing scenario net value under hard authorization/reversibility controls, shared-asset loss, reviewer capacity, cost, availability, dependencies and CVaR.

Constrained optimization

Audit AI configuration release integrity

Audit that the exact immutable AI configuration bundle evaluated and approved is the bundle exposed in every staged rollout, with consecutive parent lineage, complete blast-radius declaration, effective runtime controls, monotone traffic and a tested prior-version rollback path.

Causal inference & experiment design

Audit AI data rights provenance integrity

Audit every AI training, fine-tuning, retrieval, evaluation, logging and persisted-output use against an immutable rights grant and the complete derivative lineage, including time, revocation, deletion, purpose, jurisdiction, consent, derivative and evidence gates.

Statistical audit & measurement

Audit AI evaluation contamination integrity

Audit frozen AI evaluation suites for temporal or answer leakage, model-version mismatch, incomplete pre-label predictions, weak label provenance, missing subgroup support, cross-suite case reuse and near-duplicate content components before evaluation scores are trusted.

Forecasting & survival

Audit AI output IP provenance integrity

Audit aggregate AI outputs against the exact model and provider terms effective at generation, pre-commercialization similarity evidence, counsel-owned ownership/use rules, human review and any claimed indemnity coverage.