Understanding Your AI Agent's Behavior with SafeClaw Analytics

Authensor Team · 2026-02-13

Understanding Your AI Agent's Behavior with SafeClaw Analytics

You can't improve what you can't measure. SafeClaw's analytics dashboard transforms raw session data into actionable insights about how your AI agents behave — what they do most, where they get blocked, and where the real risks lie.

Beyond the Activity Log

The session viewer shows you what happened in a single session. Analytics shows you what happens across all sessions — the patterns, the trends, the anomalies. It answers questions that no single session can:

Which action types trigger the most escalations?
Are my agents getting more or less risky over time?
Which policy rules fire most frequently?
How much time do my agents spend waiting for human approval?
What's the average cost per session?

These questions are crucial for tuning your safety configuration. Without analytics, policy tuning is guesswork. With analytics, it's data-driven.

Key Metrics

The analytics dashboard is organized around four metric categories:

Action Distribution — A breakdown of all agent actions by type (file read, file write, shell command, network request, etc.) over configurable time windows. This tells you what your agents actually spend their time doing. We've seen users surprised to learn that 60% of their agent's actions are file reads — information that changes how they think about policy priorities. Decision Distribution — How many actions were allowed, denied, and escalated, broken down by action type and policy rule. A high deny rate on a specific rule might mean the rule is too aggressive, or it might mean your agents are consistently attempting something they shouldn't. The distinction matters, and the data helps you tell the difference. Risk Trends — Session risk scores over time, with highlights for sessions that exceeded warning or critical thresholds. Trending risk scores help you spot systemic issues — a new agent version that's consistently riskier, a project that generates more risk signals than others, or a time of day when agents behave differently. Response Times — How long escalations wait for human approval, broken down by time of day, action type, and severity. Long response times indicate either insufficient human coverage or escalation policies that trigger too often. Either way, the data points you toward the fix.

Custom Dashboards

The default dashboard covers the most common use cases, but every team is different. SafeClaw's analytics supports custom widgets that you can arrange into personalized dashboards.

Widgets are configured in your SafeClaw settings:

``yaml


analytics:
  widgets:
    - type: bar_chart
      metric: actions_by_type
      period: 7d
    - type: line_chart
      metric: risk_score_trend
      period: 30d
    - type: table
      metric: top_denied_actions
      limit: 10

Data Access

All analytics data is accessible through the SafeClaw API, not just the dashboard. This means you can build custom visualizations, integrate with existing monitoring tools (Grafana, Datadog, etc.), or export data for deeper analysis.

The API supports the same time ranges, filters, and aggregations as the dashboard UI. Full API documentation is in our docs.

Privacy-First Analytics

Analytics in SafeClaw operates on aggregated, redacted data. Individual action parameters are not included in analytics aggregations — only action types, decisions, and metadata. Secrets that were redacted during the session remain redacted in analytics data.

All analytics computation happens locally. Your data never leaves your machine for analytics purposes. If you're using SafeClaw in a team setup, each team member sees analytics only for their own sessions unless explicitly shared.

What We've Learned

Building analytics has given us fascinating insights into how AI agents are used in practice. We've learned that the average coding agent session includes 47 actions. We've learned that agents are denied most often for attempting to write outside workspace boundaries. And we've learned that most escalations are approved — suggesting that agents generally have good intentions but occasionally need a human sanity check.

Explore the analytics dashboard in your SafeClaw installation, or check out the analytics API in our documentation. Source code is on GitHub.