How to Monitor AI Agent Actions in Production

Deploying AI agents without monitoring is flying blind — you need real-time visibility into what actions agents attempt, which policies are triggered, and whether anomalous patterns emerge. SafeClaw by Authensor logs every action evaluation in a hash-chained audit trail and exposes metrics you can route to your existing monitoring stack. Every allow and deny decision is recorded with sub-millisecond precision, giving you complete observability over your AI agents in production.

Quick Start

npx @authensor/safeclaw

Scaffolds a .safeclaw/ directory. Configure monitoring as described below.

Step 1: Enable Comprehensive Audit Logging

Configure SafeClaw to log every action evaluation with full context:

# .safeclaw/config.yaml
audit:
  enabled: true
  hashChain: true
  destination: "stdout"  # Or file path, or webhook URL
  format: "jsonl"
  fields:
    - timestamp
    - action
    - effect
    - matchedRule
    - evaluationDurationMs
    - agentId
    - sessionId
    - requestDetails

Each log entry is a structured JSON object:

{
  "timestamp": "2026-02-13T14:23:01.847Z",
  "action": "file.write",
  "effect": "deny",
  "matchedRule": "block-config-writes",
  "evaluationDurationMs": 0.23,
  "agentId": "coding-assistant-01",
  "sessionId": "sess_abc123",
  "requestDetails": {
    "path": ".env.production"
  }
}

Step 2: Route Logs to Your Monitoring Stack

SafeClaw's JSONL output integrates with any log aggregation system. Route it to your existing stack:

Datadog:

# datadog-agent.yaml
logs:
  - type: file
    path: /app/.safeclaw/audit.jsonl
    service: safeclaw
    source: ai-agent-safety

Grafana Loki:

# promtail config
scrape_configs:
  - job_name: safeclaw
    static_configs:
      - targets: [localhost]
        labels:
          job: ai-agent-safety
          __path__: /app/.safeclaw/audit.jsonl

ELK Stack:

# filebeat.yml
filebeat.inputs:
  - type: log
    paths:
      - /app/.safeclaw/audit.jsonl
    json.keys_under_root: true
    json.add_error_key: true

Step 3: Build Key Dashboards

Create dashboards that answer critical operational questions:

Action Volume Dashboard:

Metrics to track:

Total actions evaluated per minute

Allow rate vs. deny rate (ratio)

Actions by type (file.write, shell.execute, api.call, etc.)

Actions by agent ID

Evaluation latency (p50, p95, p99)

Metrics to track:

Total denials per minute

Denials by rule (which rules fire most)

Denials by agent (which agents hit boundaries most)

Denial spike detection (anomaly)

New action types never seen before (zero-day detection)

Metrics to track:

Rules that have never been triggered (potential dead rules)

Rules triggered most frequently (potential candidates for optimization)

Default deny catch-all triggers (actions not covered by specific rules)

Step 4: Set Up Health Checks

Monitor that SafeClaw itself is running correctly:

# .safeclaw/config.yaml
health:
  enabled: true
  endpoint: "/health"
  port: 9100
  checks:
    - policyLoaded: true
    - auditWritable: true
    - hashChainValid: true

Add the health check to your monitoring:

# prometheus scrape config
scrape_configs:
  - job_name: safeclaw-health
    static_configs:
      - targets: ['localhost:9100']
    metrics_path: /health

Step 5: Monitor for Anomalies

Set up anomaly detection rules for unusual agent behavior:

# Alert rule examples (Prometheus/Grafana format)
groups:
  - name: safeclaw-anomalies
    rules:
      - alert: HighDenialRate
        expr: rate(safeclaw_denials_total[5m]) > 10
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "AI agent hitting deny rules frequently"

- alert: NewActionType
        expr: safeclaw_new_action_types_total > 0
        labels:
          severity: critical
        annotations:
          summary: "AI agent attempting previously unseen action type"

Why SafeClaw

• 446 tests ensuring audit logging and metrics emission are reliable
• Deny-by-default — even unmonitored edge cases are blocked by default
• Sub-millisecond evaluation — monitoring adds negligible latency to your agent's operations
• Hash-chained audit trail — tamper-proof logs for compliance and forensics
• Works with Claude AND OpenAI — one monitoring setup covers all your agents regardless of provider

Cross-References

• How to Set Up Alerts for Dangerous AI Agent Actions
• Dashboard API Reference
• Tamper-Proof Audit Trail Explained
• How to Log Every AI Agent Action for Compliance