Below is a practical, actionable guide to monitoring AI agent performance — covering what to measure, how to instrument systems, methods for evaluation, alerting, and continuous improvement. Use what’s relevant for your agent (chatbot, recommendation engine, autonomous controller, trading bot, etc.).

1. Define objectives and SLIs (service-level indicators)

• Start from business goals: user satisfaction, task success rate, revenue, safety, latency, cost.
• Translate goals into measurable SLIs (e.g., “task completion rate ≥ 90%”, “mean response latency ≤ 300 ms”, “user satisfaction score ≥ 4/5”).
• For each SLI define an SLO (target) and an error budget (acceptable breach).

2. Key metric categories

• Performance & reliability
  • Latency (p50, p90, p99), throughput (requests/sec), availability/uptime.
  • Error rates (exceptions, failed calls, timeouts).
  • Resource usage (CPU/GPU, memory, I/O, cost per request).
• Accuracy & effectiveness
  • Task success / completion rate (binary or graded).
  • Precision, recall, F1, accuracy (for classification).
  • BLEU/ROUGE/METEOR or semantic similarity (for generative text tasks).
  • Regression/MAE/RMSE (for numeric predictions).
• Utility & business impact
  • Conversion rate, click-through rate (CTR), revenue per user.
  • Time-to-success, reduction in manual work.
• User experience
  • Customer satisfaction (CSAT), Net Promoter Score (NPS), thumbs up/down, session length, re-engagement.
  • Response appropriateness / helpfulness ratings (explicit or inferred).
• Safety, fairness & compliance
  • Rate of unsafe or disallowed outputs, hallucination rate.
  • Bias/fairness metrics (disparate impact, unequal error rates across groups).
  • Privacy/PII exposure incidents and data leakage alerts.
• Concept & data drift
  • Input distribution drift (feature-statistics divergence, population shifts).
  • Label drift (changes in ground-truth distribution).
  • Model output drift (changes in prediction distribution).
• Explainability & transparency
  • Fraction of decisions with available/explanatory traces (e.g., rationale produced).
  • Confidence calibration (expected vs. observed accuracy by confidence bin).

3. Instrumentation: logging & observability

• Structured logs: include request id, timestamp, user/session id (pseudonymized), model version, prompt, metadata, latency, outcome, confidence score, safety flags.
• Traces & distributed tracing: to measure latency across microservices and identify bottlenecks.
• Metrics pipeline: export counters/gauges/histograms to Prometheus, Datadog, Cloud Monitoring, etc.
• Store examples: sample inputs/outputs for manual review (with privacy safeguards).
• Telemetry sampling: full logging for errors + sampled logs for normal traffic (to control cost).
• Label collection: capture ground truth when available (user corrections, follow-up signals, human labels).

4. Real-time monitoring and alerts

• Dashboards: latency, error rate, success rate, throughput, recent user ratings, model version rollout status.
• Alerts: trigger on SLO breaches, sudden drops in success rate, data drift score crossing threshold, safety incidents, or high hallucination rates.
• Escalation playbooks: automated rollback to previous model version, degrade to a safe fallback, or route to human-in-the-loop.

5. Evaluation: offline and online

• Offline evaluation
  • Holdout test sets; ideally stratified and representative + recent (“fresh”) data.
  • Use multiple metrics; evaluate across subgroups for fairness.
  • Stress tests: adversarial inputs, edge cases, prompt injections.
  • Simulations for agents that act over time (trajectory-based evaluation).
• Online evaluation
  • A/B testing / canary rollout to compare models on live traffic.
  • Progressive rollouts by percentage or by user cohort.
  • Continuous evaluation using real user feedback (explicit ratings, conversions, downstream signals).
• Human-in-the-loop evaluation
  • Regular human review of sampled outputs, error annotation, safety review panels.

6. Drift detection & model retraining triggers

• Compute divergence scores (e.g., KL divergence, population stability index) on input features and on model outputs.
• Monitor model performance on a “validation stream” of recent labeled examples.
• Define retraining triggers: sustained drop in accuracy, drift beyond threshold, or regular cadenced retrain (weekly/monthly) depending on domain.

7. Safety, hallucination and content controls

• Monitor hallucination rate: measure factuality vs. ground truth where possible (e.g., retrieval+generation pipelines).
• Use guardrails: deterministic checks, retrieval grounding, safety filters, blacklists, and prompt-level constraints.
• Log safety incidents and implement a review workflow to update filters and model prompts.

8. Versioning, reproducibility & rollback

• Version models, prompts, and data schemas; include model version in logs and metrics.
• Keep deployment artifacts to allow rollbacks when issues appear.
• Maintain a reproducible pipeline for training, evaluation, and deployment.

9. Privacy, compliance and security considerations

• Minimize sensitive data logged; pseudonymize or hash identifiers.
• Avoid storing full user content unless necessary and authorized; use redaction.
• Ensure monitoring data access controls and audit logs.

10. Tools & technologies (examples)

• Metrics & alerting: Prometheus + Grafana, Datadog, New Relic, Cloud Monitoring.
• Logging & tracing: ELK/OpenSearch + Kibana, Splunk, Jaeger, Honeycomb.
• A/B testing & experimentation: LaunchDarkly, Optimizely, internal feature flags.
• Data pipelines: Kafka, Kinesis, BigQuery, Snowflake for storage and analysis.
• MLOps: MLflow, Kubeflow, Seldon, BentoML, Tecton for feature infra and model serving. (Choose tools that match your stack and compliance needs.)

11. Example monitoring checklist (quick start)

• Instrument requests with model version, latency, outcome, and user feedback flag.
• Create dashboards: traffic, latency p99, error rate, task success trend, user rating trend.
• Implement alerts for abrupt drops in success rate (>5% in 1 hour) and data drift outliers.
• Set up canary rollout for new models and automatic rollback on SLO breach.
• Start periodic human review of 50 random samples/day and 100 error samples/week.
• Log and review safety incidents daily; maintain an incident response runbook.

12. Continuous improvement loop

• Use monitoring signals to prioritize fixes: data gaps, prompt tuning, training data augmentation, safety rules.
• Maintain a backlog for labeling and retraining; prioritize high-impact failure modes.
• Regular post-mortems on incidents and update SLOs as the product evolves.

13. Example metrics by agent type (pick applicable ones)

• Conversational agent: response latency, intent classification accuracy, task completion rate, user rating, fallback-to-human rate, inappropriate content rate.
• Recommendation agent: CTR, conversion rate, diversity/novelty, offline NDCG, cold-start coverage.
• Autonomous control agent: safety violations, time-to-goal, collision rate, control latency, reward attainment.
• Financial/trading agent: P&L, Sharpe ratio, drawdowns, trade execution latency, compliance flag rate.

14. Practical tips

• Start small: instrument a few high-value metrics and expand.
• Prioritize signals tied to business impact and user safety.
• Monitor trends, not just spikes — slow degradation is common and dangerous.
• Keep human reviewers in the loop for capturing nuanced failure modes.
• Automate where possible, but keep human oversight for safety-critical decisions.

If you want, I can:

• Create a sample dashboard layout and specific Prometheus/Grafana metrics (with metric names and alert rules).
• Provide a template log schema for your agent (fields to capture) and sampling policy.
• Suggest an alerting/rollback playbook tailored to your agent type.

Which (if any) of those would you like me to prepare next?