26 lines
4.6 KiB
JSON
26 lines
4.6 KiB
JSON
{
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"schema_version": 1,
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"saved_at": "2026-08-07T05:00:13.6014045Z",
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"content_sha256": "41b99edbfc8aa53b731f3c2af196dde7fb3a4972ba5772dc847bd36f3f9cbfd0",
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"result": {
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"title": "Baseline Behavioral Profiling for AI Agents | Learn | Authensor | Authensor",
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"url": "https://www.authensor.com/learn/baseline-behavioral-profiling-for-ai-agents",
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"snippet": "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.",
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"content": "Baseline Behavioral Profiling for AI Agents | Learn | Authensor | Authensor\n\n← Back to Learn\nmonitoring agent-safety explainer\n\nBaseline Behavioral Profiling for AI Agents\n\nAuthensor\n\nA 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.\n\nProfile Components\n\nA complete behavioral profile includes:\n\nAction 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.\n\nTemporal patterns : When the agent is active, how its activity varies by hour and day, and whether it has periodic patterns like batch processing cycles.\n\nResource access patterns : Which resources the agent accesses most frequently, which combinations of resources appear together, and the typical access sequence.\n\nResponse characteristics : Typical output length, token usage, latency distribution, and error rate.\n\nBuilding the Baseline\n\nCollect 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.\n\nDuring 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.\n\nAdaptive Baselines\n\nAgent 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.\n\nThe 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.\n\nPer-Agent vs Fleet Baselines\n\nBuild 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.\n\nUsing Profiles for Alerting\n\nConfigure 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.\n\nBehavioral profiles are the foundation of anomaly detection. Invest the time to build them properly.\n\nKeep learning\n\nExplore more guides on AI agent safety, prompt injection, and building secure systems.\nView All Guides",
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"content_type": "text/html",
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"query": "How is the documentation of baselines and expected normal behavior for AI agents implemented in practice?",
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"language": "en-US",
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"round": 1,
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"fetched": true,
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"relevant": true,
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"relevance": 0.9542857142857143,
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"source_quality": "primary",
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"source_quality_score": 0.864,
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"actionable": true,
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"covered_gap_ids": [
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"KG-001"
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],
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"assessment_reason": "The source provides a detailed explanation of how to implement behavioral profiling for AI agents, including the components of a baseline, the process of building a baseline, and the use of adaptive baselines. It also includes practical steps for monitoring and alerting based on deviations from the baseline. This directly addresses the question of how baselines and expected normal behavior are documented and implemented in practice."
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}
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}
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