Building Observable AI Agents: A Practical Guide

Observability is the foundation of AI agent security. Without visibility into what your agents are doing, security is guesswork. This guide covers practical approaches to building observable AI agents.

What to Observe

Comprehensive agent observability covers several dimensions:

Inputs — Everything that enters the agent's context: user messages, retrieved documents, API responses, system prompts. This is your audit trail for understanding what influenced agent behavior.

Outputs — Everything the agent produces: responses, tool invocations, generated content. Critical for detecting data leakage and unauthorized actions.

Actions — Every tool use, API call, file access, and external communication. The record of what your agent actually did.

Reasoning — Where available, the agent's intermediate reasoning steps. Invaluable for debugging unexpected behavior.

Performance — Latency, token usage, error rates. Operational metrics that affect user experience and cost.

Structured Logging

Unstructured logs are nearly useless for security analysis. Structure your logs with consistent schemas:

{
  "timestamp": "2026-01-31T10:30:00Z",
  "session_id": "sess_abc123",
  "agent_id": "customer-support-v2",
  "event_type": "tool_invocation",
  "tool_name": "send_email",
  "parameters": {
    "to": "[REDACTED]",
    "subject": "Re: Your inquiry"
  },
  "result": "success",
  "latency_ms": 234
}

Note the PII redaction—observability shouldn't compromise user privacy.

Behavioral Baselines

Raw logs tell you what happened. Behavioral baselines tell you if it's normal.

For each agent type, establish:

Typical session length and action count

Common tool usage patterns

Normal data access patterns

Expected external communication endpoints

Deviations from these baselines are potential security events. An agent that normally makes 5-10 tool calls suddenly making 500 deserves investigation.

Alerting Strategy

Not every anomaly is an attack. Effective alerting requires:

Severity tiers — Distinguish between "interesting" and "critical"

Correlation — Single anomalies might be noise; multiple concurrent anomalies are likely real

Context enrichment — Alerts should include enough information to investigate

Actionable thresholds — Set thresholds that minimize alert fatigue

Moltwire handles this complexity automatically, correlating signals across multiple dimensions to surface genuine threats while minimizing false positives.

From Observability to Security

Observability is necessary but not sufficient for security. The data needs analysis:

Pattern matching — Detecting known attack signatures

Anomaly detection — Identifying deviations from normal behavior

Threat correlation — Connecting signals across agents and time

Response automation — Taking action when threats are detected

This is what Moltwire provides—the layer that transforms observability data into actionable security intelligence.