A behavioral profile is a statistical description of how an agent normally operates. It captures the patterns, frequencies, and distributions of an agent's actions during normal operation. Without a baseline, you cannot distinguish between normal variation and genuine anomalies.
A complete behavioral profile includes:
Action distribution: The frequency of each action type as a proportion of total actions. A research agent might be 60% search, 25% read, 10% summarize, and 5% other.
Temporal patterns: When the agent is active, how its activity varies by hour and day, and whether it has periodic patterns like batch processing cycles.
Resource access patterns: Which resources the agent accesses most frequently, which combinations of resources appear together, and the typical access sequence.
Response characteristics: Typical output length, token usage, latency distribution, and error rate.
Collect data during a burn-in period of known-good operation. The duration depends on the agent's activity volume and variability. A high-traffic agent may need only a few days. A low-traffic agent with weekly cycles may need several weeks.
During the burn-in period, review samples of agent behavior manually to confirm that the data represents genuinely normal operation. Contaminated baselines that include anomalous behavior during the burn-in period will suppress future detection.
Agent behavior changes over time as capabilities are added, models are updated, and workloads shift. Static baselines become stale. Authensor's Sentinel uses EWMA to maintain adaptive baselines that track gradual changes while remaining sensitive to sudden shifts.
The decay factor controls how quickly the baseline adapts. A high decay factor (0.9+) creates a stable baseline that resists change. A low decay factor (0.5) creates a responsive baseline that adapts quickly but may absorb anomalies.
Build profiles at two levels. Per-agent baselines capture individual agent behavior. Fleet baselines capture the collective behavior of all agents of a given type. Compare individual agents against both their own baseline and the fleet baseline. An agent that deviates from its own baseline but matches the fleet may just be experiencing normal variation. An agent that deviates from both is more likely anomalous.
Configure alerts that trigger when an agent's observed behavior diverges from its profile by more than a threshold. Express thresholds in standard deviations or percentile ranks.
Behavioral profiles are the foundation of anomaly detection. Invest the time to build them properly.
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