How do I monitor and log AI agent performance?
Key Metrics to Track
Focus on metrics that reflect both technical and business performance. Technical metrics include latency (time to first token, total response time), error rates, and throughput. Business metrics include task completion rate, user satisfaction, and cost per interaction.
Also monitor model-specific metrics like token usage, which affects cost, and the frequency of fallback to human or simpler models. Set up alerts for anomalies, such as a sudden spike in errors or latency.
- Latency: time from request to response.
- Error rate: percentage of failed requests.
- Token usage: input and output tokens per request.
- Task success rate: how often the agent completes the intended task.
- Cost per interaction: total cost divided by number of interactions.
Logging Best Practices
Log every interaction with a unique ID, timestamp, user ID (if applicable), input, output, and any tool calls made. This helps with debugging and auditing. Use structured logging (JSON) so logs are easy to parse and query.
Centralize logs using tools like ELK Stack (Elasticsearch, Logstash, Kibana), Grafana Loki, or cloud-native solutions like AWS CloudWatch. Ensure logs are retained according to your compliance requirements. Be mindful of privacy: avoid logging sensitive user data unless necessary and secure it.
Common mistakes
- Only logging errors and not successful interactions, which makes it hard to understand normal behavior.
- Not including enough context (like user ID or session ID) in logs, making it difficult to trace issues.
- Ignoring the cost of logging and storing massive amounts of data without a retention policy.
