Solutions/Use Cases/Runtime Monitoring
Use Case

Varman for AI Runtime Monitoring

87% of AI production incidents are first detected by end users — not monitoring systems. Varman establishes behavioral baselines for every agent and detects anomalies within 200 milliseconds.

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The Challenge

AI agents tested in staging behave differently in production. Real data, real user interactions, and real system load expose behaviors that never appeared in controlled testing.

Behavioral drift is silent. An agent that was 97% accurate in January can degrade to 78% by March without any deployment changes — just because the distribution of inputs shifted.

Without monitoring, security incidents compound. A prompt injection attack that succeeds once may be replicated thousands of times before anyone notices.

Traditional APM tools track latency and error rates — but they don't understand what 'wrong' looks like for an AI agent. Varman establishes semantic behavioral baselines.

● INCIDENTS DETECTED BY USERS (NOT MONITORING)

How Varman Solves It

01

Instrument Agents at Runtime

Varman's lightweight SDK wrapper (or zero-code proxy mode) captures every invocation, response, token count, latency, and tool call across all your agents — without modifying agent logic or adding meaningful latency.

SDK WrapperZero-Code Proxy<5ms Overhead
02

Anomaly Detection via Behavioral Baseline

Varman builds a behavioral baseline for each agent: normal latency distribution, typical response patterns, expected tool call sequences, and acceptable error rates. Deviations trigger alerts within 200ms.

Behavioral Baseline<200ms DetectionSemantic Analysis
03

Automated Alerting + Response

Alerts route to PagerDuty, Slack, or your SIEM with full context — what the anomaly was, which agent, what inputs triggered it, and what the expected behavior was. Optional auto-remediation: throttle, restart, or quarantine.

PagerDuty IntegrationSlack AlertsAuto-Remediation
Live Demo

Runtime Health Monitor

varman — runtime-health-monitor
● LIVE · REFRESHING 2s
AgentStatusLatencyError%TrendLast Anomaly
sales-prospector-v2HEALTHY312ms0.2%none
fin-reconcilerHEALTHY847ms0.0%14h ago
legal-drafterDEGRADED2840ms4.7%just now
hr-onboarding-botHEALTHY423ms0.1%3d ago
data-pipeline-agentHEALTHY156ms0.0%none
support-triage-v3CRITICAL8200ms12.3%just now

Key Capabilities

Behavioral Baseline Profiling

Automatically builds a normal behavior model for each agent based on 7 days of production traffic.

Real-Time Anomaly Detection

Sub-200ms detection of behavioral deviations — before they cascade into user-visible incidents.

Latency/Error/Token Tracking

Full observability stack: p50/p95/p99 latency, error rates, token consumption, and tool call frequency.

Cascade Failure Detection

Detects when one agent's anomalous output is causing downstream agents to fail — before the cascade completes.

PagerDuty/Slack Alerting

Native integrations with PagerDuty, Slack, OpsGenie, and any webhook endpoint — with full context in every alert.

Root Cause Analysis

One-click root cause analysis traces anomalies back to specific inputs, model changes, or data source shifts.

Measured Outcomes

0ms

Maximum anomaly detection latency

0.9%

Monitoring uptime — always-on coverage

0%

AI incidents detected before any user impact

0%

Reduction in Mean Time To Resolution (MTTR)

Start solving AI Runtime Monitoring today

Deploy behavioral monitoring across your entire agent fleet in under an hour.

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