Short answer Monitor across four dimensions: runtime reliability (latency, errors, availability), model quality (accuracy, task success, hallucination rate, calibration), data/model drift, and business/user impact. Instrument everything, run continuous offline + online evaluations, alert on meaningful degradations, and close the loop with retraining / human review.

Detailed, actionable plan

1. Define what “good” means

• Business KPIs: conversion, retention, time-to-resolution, cost-per-task.
• Agent-specific KPIs: episode success rate, average steps to completion, reward per episode, human override rate.
• Model-quality KPIs: accuracy/F1, top-N correctness, hallucination rate, response appropriateness, calibration (confidence vs correctness).
• Reliability KPIs: latency P50/P95/P99, throughput (reqs/sec), error rate, uptime.
• Safety/Compliance: policy-violation count, toxic/biased outputs, PII leakage incidents.

2. Instrumentation & telemetry (what to log)

• Request metadata: timestamp, user id (hashed/pseduonymized), request type, model & version, routing/canary flags.
• Inputs & outputs (or hashes if PII): prompt id, response id, truncated text or embeddings, confidence scores, retrieval sources, grounded evidence ids.
• Execution traces: latency per component (tokenization, generation, retrieval, post-process), GPU/CPU usage.
• Outcome labels: success/failure, human feedback, business outcome (conversion, ticket closed).
• Errors & exceptions: stack traces, retry counts, fallback used.
• Sampling/retention: full logs for a small fraction; hashed or redacted content for most; retention policy for PII.

3. Offline evaluation & test suites

• Unit tests for core behaviors (edge cases, prompt templates).
• Benchmark datasets / golden references — compute accuracy, BLEU/ROUGE, F1 where applicable.
• Safety tests (toxicity lists, persona leakage, jailbreak prompts).
• Adversarial and fuzz testing (mutations, paraphrases, broken inputs).
• Synthetic scenarios for multi-step agents (simulate end-to-end episodes).

4. Online evaluation & deployment strategy

• Canary + shadow mode: run new agent version on a small % of traffic and compare metrics without affecting users.
• A/B testing: measure business KPIs and user satisfaction.
• Progressive rollout with automated rollback rules.
• Live “health” checks: synthetic probes that run representative tasks continuously.

5. Drift & calibration detection

• Input/data drift: population stability index (PSI), KL divergence, distribution comparison on features/embeddings.
• Label/target drift: change in ground-truth distribution or success rates.
• Model performance drift: rolling-window accuracy or reward curves.
• Calibration: Expected Calibration Error (ECE) to compare predicted confidences to empirical accuracy.
  • Example ECE (binned): compute |accuracy_bin − confidence_bin| weighted by bin size and average across bins.

6. Quality-specific automated checks

• Hallucination detection: check for unsupported facts via retrieval grounding, verify named entities against knowledge base, use model agreement (ensemble) or a verifier model.
• Consistency checks: same prompt -> deterministic/consistent answers (or acceptable variance).
• Toxicity/policy checks: run content classifiers on outputs and log scores.
• Red-teaming & adversarial prompts scheduled regularly.

7. Alerts & runbooks

• Alert examples:
  • Latency P95 > 2s for 5 minutes.
  • Error rate > 1% for 10 minutes.
  • Episode success rate drops by > 5 percentage points vs baseline.
  • Hallucination rate increases by > 20% relative to last 7-day average.
  • PSI > 0.2 on important feature (indicates drift).
• For each alert: attach runbook with quick checks (recent deploys, infrastructure issues, input distribution change, external API failures) and rollback criteria.

8. Observability stack (categories + common examples)

• Metrics & dashboards: Prometheus + Grafana, Datadog, New Relic.
• Logging & traces: ELK / OpenSearch, Splunk, Honeycomb, Jaeger.
• Model monitoring: Arize, Evidently, WhyLabs, Fiddler, Weights & Biases (tracking), MLflow for model registry.
• Error tracking: Sentry.
• Human feedback and annotation: Label studio, internal tools, integrated feedback flows.

9. Continuous learning & feedback loop

• Capture labeled failures and human corrections; store in versioned dataset.
• Triage for label quality and common failure modes.
• Retrain triggers: performance drop beyond threshold, significant drift, or accumulated labeled errors.
• Track dataset lineage and model versions; test candidate models on holdout sets, canaries, and regression tests before full rollout.

10. Governance, privacy & compliance

• Redact or hash PII before storing logs; maintain a data retention policy.
• Audit trails: model version, training data snapshot, evaluation metrics for each deployment.
• Periodic audits for bias, safety, and privacy compliance.

11. Root Cause Analysis (RCA) approach

• Correlate metric changes with: recent deploys, input distribution shift, third-party API changes, system resource constraints, config changes.
• Slice metrics by user segment, region, prompt template, and model version to isolate cause.
• Use traces and example requests to reproduce failure.

Quick checklist to implement immediately

• Instrument structured logs + unique request IDs.
• Add latency and error metrics to metrics backend and build top-level dashboard.
• Deploy synthetic probes that exercise typical user flows every 1–5 minutes.
• Start canary/ shadow deployments for every model release.
• Implement a simple alert (e.g., success rate or P95 latency).
• Store human-labeled failures in versioned dataset for retraining.
• Apply automated safety filters and PII redaction before logging.
• Maintain model registry and link each live model to its training dataset snapshot.

Example monitoring rule (concrete)

• “If rolling 30-day episode success rate for model-v3 drops by ≥ 5 percentage points compared to model-v3’s 30-day baseline AND P95 latency < 2s, trigger critical alert, send to on-call, and revert traffic to previous stable model.”

Final tips

• Start small: prioritize the 3–5 metrics that map directly to business/user value and instrument them well.
• Automate what you can (canaries, alerts, synthetic tests) so humans focus on high-value triage and labeling.
• Measure both technical and human-facing outcomes — a model can be “accurate” but harmful or annoying to users.
• Treat monitoring as code: version dashboards, alerts, and runbooks alongside model code.

If you want, I can produce a one-page dashboard design with the specific panels and alert thresholds mapped to your use-case (chat agent, task automation agent, or robotics agent).