AI Agent Runtime Risk & Action Governance Monitor

Runtime governance for autonomous agents: analyze observed tool calls, permissions, approvals and side effects, compare behavior across runs, and return explicit ALLOW/REVIEW/BLOCK decisions with evidence. Returns structured, machine-readable results for repeatable research.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "eventId": "evt-101",
      "sessionId": "sess-44",
      "agent": "procurement-agent",
      "tool": "payments.create",
      "action": "create_payment",
      "resource": "vendor:acme",
      "permission": "payments:write",
      "approvalStatus": "missing",
      "sideEffect": "financial",
      "riskType": "unapproved_financial_action",
      "riskScore": 92,
      "sensitiveData": false,
      "timestamp": "2026-09-20T14:04:00Z"
    },
    {
      "eventId": "evt-102",
      "sessionId": "sess-44",
      "agent": "procurement-agent",
      "tool": "crm.search",
      "action": "read_contacts",
      "resource": "crm:contacts",
      "permission": "contacts:read",
      "approvalStatus": "not_required",
      "sideEffect": "read",
      "riskType": "sensitive_data_access",
      "riskScore": 48,
      "sensitiveData": true,
      "timestamp": "2026-09-20T14:04:04Z"
    },
    {
      "eventId": "evt-103",
      "sessionId": "sess-44",
      "agent": "procurement-agent",
      "tool": "shell.exec",
      "action": "execute_command",
      "resource": "host:worker-2",
      "permission": "shell:execute",
      "approvalStatus": "denied",
      "sideEffect": "execution",
      "riskType": "privilege_escalation_attempt",
      "riskScore": 96,
      "sensitiveData": false,
      "timestamp": "2026-09-20T14:04:08Z"
    }
  ],
  "previousItems": [
    {
      "eventId": "evt-091",
      "sessionId": "sess-41",
      "agent": "procurement-agent",
      "tool": "crm.search",
      "action": "read_contacts",
      "resource": "crm:contacts",
      "permission": "contacts:read",
      "approvalStatus": "not_required",
      "sideEffect": "read",
      "riskType": "sensitive_data_access",
      "riskScore": 35,
      "sensitiveData": true,
      "timestamp": "2026-09-13T14:04:04Z"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.
• allowedTools — Allowed tools (optional): Optional exact tool allowlist. When supplied, a runtime call to another tool is treated as a governance violation.
• deniedPermissions — Denied permissions (optional): Permission or scope names that must never appear in executed runtime events.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.018000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Detailed documentation

Turn AI-agent execution traces into evidence-backed governance decisions.

This Actor analyzes what an autonomous agent actually did at runtime: tool calls, actions, permissions, approval state, sensitive-data access, side effects and behavior drift. It returns event-level risk findings plus an executive ALLOW / REVIEW / BLOCK decision that can feed security, observability, compliance and agent-control workflows.

Why use this Actor

Runtime behavior is the layer static security cannot prove.

Why this is different

Most agent-security scanners inspect repositories, prompts, MCP manifests or workflow configuration before execution. Those checks matter, but they cannot prove what happened after an agent started acting.

This Actor is built for the runtime layer. Supply agent events directly or point it at an Apify Dataset. It evaluates executed behavior against an optional policy and, when a previous snapshot is supplied, detects new permissions and changing risk patterns.

Key features

What it detects

• Unapproved high-impact actions such as financial, destructive, execution, deployment or privilege-changing side effects.
• Actions executed after approval was denied.
• Tool allowlist violations when a runtime call uses a tool outside the permitted set.
• Denied permission usage for explicitly forbidden scopes.
• Privilege creep when an agent starts using a new high-impact permission compared with the baseline period.
• Sensitive-data access when the supplied trace marks an event as containing or touching sensitive data.
• Runtime risk drift between current and previous execution snapshots.
• Concentrated agent risk through per-agent summaries, blocked/review counts and new permissions.

Input

Use currentItems for inline runtime events or currentDatasetId to analyze an Apify Dataset. A previous snapshot is optional through previousItems or previousDatasetId.

Useful event fields include:

{
  "eventId": "evt-103",
  "sessionId": "sess-44",
  "agent": "procurement-agent",
  "tool": "shell.exec",
  "action": "execute_command",
  "resource": "host:worker-2",
  "permission": "shell:execute",
  "approvalStatus": "denied",
  "sideEffect": "execution",
  "riskType": "privilege_escalation_attempt",
  "riskScore": 96,
  "sensitiveData": false,
  "timestamp": "2026-09-20T14:04:08Z"
}

Field aliases are normalized conservatively, so common variants such as toolName, agent_id, trace_id, approval_status, permissionScope and side_effect can also be used.

Optional policy controls

You can make the Actor evaluate traces against your own governance rules:

• allowedTools: exact tool allowlist. Calls outside it become findings.
• deniedPermissions: scopes that must never appear in executed events.
• requireApprovalFor: side-effect classes that require explicit approval.
• policy.thresholds.review: score at which an event becomes REVIEW.
• policy.thresholds.block: score at which an event becomes BLOCK.

The defaults are intentionally conservative for financial, destructive, privilege, credential, execution, deployment and external-write actions.

Output

One decision-ready report is written to the default Dataset and to the INTELLIGENCE_REPORT key-value-store record.

Core output includes:

• policyDecision: overall ALLOW, REVIEW or BLOCK.
• risk_score / agentRuntimeRiskScore: portfolio-level runtime risk.
• criticalCount, highCount, blockedActionCount, reviewActionCount.
• privilegeCreepCount, approvalViolationCount, sensitiveDataEventCount.
• runtimeDrift: baseline availability, risk delta and blocked-action delta.
• topViolationTypes: dominant governance failures.
• agentRiskSummary: per-agent event volume, max risk and new permissions.
• findings: evidence-backed event findings with remediation guidance.
• agentAction: machine-readable recommended next action.

Example finding:

{
  "eventId": "evt-103",
  "agent": "procurement-agent",
  "tool": "shell.exec",
  "permission": "shell:execute",
  "runtimeRiskScore": 100,
  "severity": "CRITICAL",
  "policyDecision": "BLOCK",
  "violations": ["ACTION_AFTER_DENIED_APPROVAL", "PRIVILEGE_CREEP"],
  "evidence": [
    "approval status is 'denied' for a high-impact action",
    "new high-impact permission versus baseline: 'shell:execute'"
  ],
  "remediation": [
    "Require an explicit approval token before the tool call can execute",
    "Reduce permission scope and bind elevated scopes to short-lived approval"
  ]
}

Use cases

Practical workflows

Agent observability

Feed traces from your orchestration layer on a schedule and monitor changes in blocked actions, sensitive-data access and privilege use.

MCP / tool-call governance

Normalize MCP calls into runtime events and use allowedTools, approval state and permission scopes to identify tools or actions that exceed policy.

Incident review

Run a suspicious session as currentItems to obtain a ranked, auditable set of findings and remediation actions.

Regression monitoring

Store each period as an Apify Dataset, then compare the latest period against a previous Dataset to detect risk and permission drift.

CI / release evidence

Run representative agent traces before and after a release. A rising risk score or new high-impact permission can become a release-review signal.

Security model

The Actor uses limited permissions and reads only the Dataset resources explicitly supplied to it. It does not require access to the rest of your Apify account. It does not execute submitted tool calls, scripts or commands; it analyzes trace records as data.

This is deliberate: a governance Actor should not become another privileged execution surface. Humanity has already invented enough of those.

Decision model

The engine combines explicit source risk scores, high-risk action terms, approval requirements, tool policy, denied scopes, sensitive-data markers and baseline permission drift. Multiple violations raise confidence in escalation without pretending that a heuristic score is a legal or security guarantee.

The result is deterministic for the same input and policy.

Pricing

Pay per event. One primary charge corresponds to one decision-ready intelligence report, not every trace row. This keeps recurring runtime governance economical even when the report summarizes many events.

Limitations

• The Actor analyzes supplied trace records; it does not intercept live agent traffic by itself.
• It is decision support, not a replacement for platform-level authorization, sandboxing or incident-response controls.
• Sensitive-data detection uses supplied flags and observable fields; it does not attempt exhaustive secret/PII discovery.
• Preserve stable identifiers across snapshots for the strongest drift analysis.

Suggested upstream sources

Any system that can emit JSON runtime events can feed this Actor: agent frameworks, observability pipelines, MCP gateways, workflow engines, API logs, audit logs or custom tracing systems.

Workflow: agent traces -> normalization -> policy evaluation -> permission/approval analysis -> runtime drift -> ranked findings -> ALLOW/REVIEW/BLOCK -> agentAction

Agent execution and AI discovery (October 2026)

Use this Actor when: Runtime governance for autonomous agents: analyze observed tool calls, permissions, approvals and side effects, compare behavior across runs, and return explicit ALLOW/REVIEW/BLOCK decisions with evidence. Returns structured,...

For AI agents and automations:

• Find this Actor in Apify Store by its use case and verify its actual input and pricing through Apify MCP (search-actors and fetch-actor-details). Availability and ranking in Apify AI are controlled by Apify; discovery is not guaranteed.
• Read the Input tab or input schema. Recognized fields include currentItems, currentDatasetId, previousItems, previousDatasetId, maxItems, previousAnalysis, valuePerImpactUnitUsd, monthlyRuns. Required fields: See the Input tab for valid example values. Do not invent records or credentials.
• Supply a small representative input first. Use Apify API, Tasks, or MCP to run and retrieve the structured dataset; inspect the output schema rather than assuming all inputs produce the same rows.
• For programmatic calls, set timeouts and maxTotalChargeUsd in run options to limit exposure. Check the Apify Pricing tab for billable events and discounts.
• Payment compatibility: this Actor is published with limited permissions and pay-per-event pricing. Apify AGI prepaid x402 tokens can execute limited-permission Actors. Direct per-request payment is a separate, conditional feature that may require additional eligibility checks and creator verification. Do not interpret this paragraph as proof of approved KYC or guaranteed payment acceptance.
• This Actor does not independently guarantee real-time access to a third-party source. Verify input provenance and important conclusions, and do not supply confidential personal data unnecessarily.