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Monitoring and Observability for AI Agents in Production

October 8, 2025•5 min read•Team 400

Launching an AI agent is the beginning, not the end.

In production, you need to know: Is it working? Is it accurate? Is it fast enough? Is it costing too much? Are there problems you haven't noticed?

Here's how to build monitoring and observability for AI agents that actually helps you operate.

What's Different About AI Monitoring

Traditional application monitoring tracks: Is the server up? How fast are requests? Are there errors?

AI agent monitoring adds: Is the agent making good decisions? Are responses accurate? Is it handling edge cases? Is it drifting from expected behavior?

You need both. Traditional infrastructure monitoring plus AI-specific monitoring.

The Monitoring Stack

Layer 1: Infrastructure Metrics

Basic health and performance:

Service health:

  • Uptime / availability
  • Request rate
  • Error rate
  • Latency distribution (p50, p95, p99)

Resource usage:

  • CPU / memory
  • API quota consumption
  • Database connections
  • Queue depths

Dependencies:

  • LLM API latency and errors
  • External service health
  • Database performance

This is table stakes. Without infrastructure monitoring, you're flying blind.

Layer 2: AI-Specific Metrics

What makes AI agents different:

Conversation metrics:

  • Conversations started / completed
  • Turns per conversation
  • Resolution rate (conversation ended with goal achieved)
  • Escalation rate (handed off to human)
  • Abandonment rate (user left without resolution)

Quality metrics:

  • Response accuracy (requires labeling)
  • Hallucination rate
  • Tone/appropriateness scores
  • Task completion rate

Cost metrics:

  • Tokens consumed per conversation
  • Cost per conversation
  • Cost per resolution

Model metrics:

  • Confidence distribution
  • Token usage patterns
  • Tool usage patterns
  • Error type distribution

Layer 3: Business Metrics

Connect to business outcomes:

For customer service agents:

  • Tickets deflected
  • Customer satisfaction (CSAT)
  • Time to resolution
  • Cost per ticket

For automation agents:

  • Tasks completed
  • Processing time vs manual baseline
  • Error rate vs manual baseline
  • Throughput

For sales agents:

  • Lead response time
  • Qualification rate
  • Conversion contribution

Building the Observability Stack

Logging Strategy

Log everything you'll need for debugging and analysis:

Conversation logs:

{
  "conversation_id": "conv_123",
  "turn_number": 3,
  "timestamp": "2025-10-08T14:32:00Z",
  "user_message": "What's my account balance?",
  "agent_response": "Your current balance is $1,234.56.",
  "tokens_used": 156,
  "latency_ms": 1230,
  "model": "gpt-4-turbo",
  "tools_called": ["account_lookup"],
  "confidence_score": 0.95
}

Decision logs:

{
  "conversation_id": "conv_123",
  "decision_type": "tool_selection",
  "options_considered": ["account_lookup", "transaction_history"],
  "selected": "account_lookup",
  "reasoning": "User asked about balance, not transactions",
  "confidence": 0.95
}

Structure logs for queryability. You'll want to slice by conversation, user, time, outcome.

Tracing

Trace requests through the full agent flow:

Request → Intent Classification → Tool Selection →
         Tool Execution → Response Generation → Delivery

Each step:
- Timing
- Inputs/outputs
- Errors
- Dependencies called

Use OpenTelemetry or similar for distributed tracing.

Dashboards

Build dashboards for different audiences:

Operations dashboard:

  • Real-time health indicators
  • Error rates and alerts
  • Queue depths
  • API quota status

Performance dashboard:

  • Latency trends
  • Throughput trends
  • Cost trends
  • Resolution rates

Quality dashboard:

  • Accuracy metrics
  • Escalation patterns
  • User feedback
  • Conversation samples

Alerting Strategy

Immediate Alerts (Wake Someone Up)

  • Agent completely unresponsive
  • Error rate > 10%
  • LLM API unavailable
  • Critical integration failure

Urgent Alerts (Address Today)

  • Error rate > 3%
  • Latency p95 > threshold
  • Resolution rate dropping
  • API quota approaching limit

Trend Alerts (Review This Week)

  • Gradual accuracy decline
  • Escalation rate increasing
  • New error patterns emerging
  • Cost per conversation increasing

Tune thresholds based on your business context. What matters varies.

Quality Monitoring

Automated Evaluation

Some quality can be measured automatically:

Response quality checks:

  • Did response match expected format?
  • Did response use appropriate tools?
  • Was response length reasonable?
  • Did response contain prohibited content?

Task completion:

  • Did user achieve stated goal?
  • Were all required steps completed?
  • Were any errors generated?

Human Review

Some quality requires human judgment:

Sampling strategy:

  • Random sample of conversations
  • All escalated conversations
  • Low-confidence responses
  • User-flagged interactions

Review process:

  • Regular cadence (daily or weekly)
  • Structured evaluation criteria
  • Feedback loop to improvement

We typically recommend reviewing 2-5% of conversations initially, adjusting based on findings.

Feedback Collection

Get signal from users:

  • Thumbs up/down on responses
  • Post-conversation surveys
  • Explicit feedback requests
  • Implicit signals (returning users, task completion)

Make feedback easy. Analyze it systematically.

Detecting Drift

AI agents can degrade over time:

Model drift: The LLM provider updates their model. Behavior changes.

Data drift: The types of requests shift. Training assumptions no longer hold.

Integration drift: Connected systems change. Data formats shift.

World drift: Business rules change. Information becomes outdated.

Detection approaches:

  • Track metric distributions over time
  • Alert on statistical anomalies
  • Compare current performance to baseline
  • Periodic re-evaluation against test sets

Operational Runbooks

Document how to respond to common issues:

High Error Rate

  1. Check LLM API status
  2. Review recent deployments
  3. Sample error logs for pattern
  4. Decide: rollback, fix forward, or degrade gracefully

Low Resolution Rate

  1. Review conversation samples
  2. Identify common failure patterns
  3. Check for data quality issues
  4. Evaluate if scope has shifted

Cost Spike

  1. Identify conversations with high token usage
  2. Check for infinite loops or retry storms
  3. Review prompt efficiency
  4. Evaluate if legitimate traffic increase

Tools and Platforms

Logging and Tracing

  • OpenTelemetry (open standard)
  • Datadog, New Relic, Dynatrace (commercial APM)
  • Azure Monitor / Application Insights (if on Azure)

LLM-Specific Monitoring

  • LangSmith (LangChain's observability platform)
  • Helicone (LLM proxy with analytics)
  • Custom dashboards work fine too

Conversation Analytics

  • Often custom-built for specific needs
  • Can integrate with customer service analytics tools

Our Approach

For AI agents we build, monitoring is part of the deliverable, not an afterthought:

  • Logging and tracing from day one
  • Dashboards tailored to the use case
  • Alerting configured before launch
  • Runbooks for common scenarios
  • Regular review cadence established

Operating AI agents well is as important as building them well.

Talk to us about AI agent operations.