ai-observability
FeaturedUse when adding Spring AI-specific model observations, token usage, latency, externally configured cost attribution, advisor telemetry, or protected prompt and completion logging. Use production-observability for general service metrics, health, logs, and OTLP setup.
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Quality Score: 88/100
Skill Content
Details
- Author
- rrezartprebreza
- Repository
- rrezartprebreza/spring-boot-skills
- Created
- 4 months ago
- Last Updated
- 6 days ago
- Language
- Java
- License
- MIT
Integrates with
Similar Skills
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ai-observability
Design privacy-aware observability for AI applications across prompts, models, retrieval, agents, tools, quality, latency, cost, tokens, errors, traces, feedback, and evaluation results. Use for production readiness and AI incident diagnosis.
observability
Backend observability patterns — structured logging, Micrometer metrics, OpenTelemetry tracing, Spring Boot Actuator, Kubernetes health probes, alerting, and dashboards. Use when user mentions logging, metrics, tracing, monitoring, health checks, or Prometheus.
agent-observability
Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.