Observability Stack — AI Agent Monitoring

Full OpenTelemetry observability stack for self-hosted infrastructure with metrics, logs, traces, and alerting. Traces every AI agent interaction from tool calls to LLM requests.

Role: Creator
Year: 2026
Stack: Grafana · Prometheus · Jaeger · OpenTelemetry · Loki · Tempo · Docker

Context

Self-hosted services need enterprise-grade monitoring without the enterprise cost. Open-source solutions exist but require careful integration and tuning to work together seamlessly. For AI agents specifically, observability is non-negotiable — every agent session is a black box without tracing.

Approach

Deployed a complete OpenTelemetry stack:

OpenTelemetry Collector: Unified data pipeline with OTLP HTTP/gRPC endpoints

Jaeger: Distributed tracing UI with dependency graph visualization

Tempo: Distributed tracing backend with trace storage and query capabilities

Prometheus: Metrics collection with 7 custom alert rules, Alertmanager routing to n8n webhooks

Loki: Log aggregation for centralized log management and analysis

Grafana: Real-time dashboards (5 pre-built), alerting integration, Alertmanager configuration

Alertmanager: 7 automated alert rules for proactive incident response, routes alerts to Telegram bot channel via n8n webhook automation

n8n Workflow: Automated alert processing and Telegram notification pipeline

Docker Compose: All services orchestrated with automated provisioning

Architecture traces every AI agent interaction — from tool call to LLM request to decision point — aggregating all data in Grafana dashboards for real-time monitoring.

Outcome

Single-pane-of-glass observability: Correlated metrics, logs, and traces across all 15 HomeLab services

AI agent visibility: Traces every tool call, LLM request, and decision point; discovered 3x LLM call optimization bug within first week (67% efficiency gain)

7 automated alert rules: Proactive incident response with Alertmanager routing to Telegram bot channel via n8n webhook automation

10k+ traces/month: Distributed tracing for AI agent sessions via Jaeger

Real-time dashboards: 5 pre-built Grafana dashboards monitoring latency, error rates, token usage

Variable latency insight: Discovered 200ms-8s tool call latency variation; reduced average tool latency from 2.5s to 0.8s via caching

Prompt engineering impact: Measured 47% efficiency improvement (8 vs. 15 tool calls) with prompt version C

Faster incident detection: Reduced mean time to detection with correlated observability signals

Real-world value: You can't improve what you can't measure — this stack ensures every AI agent session is visible, traceable, and improvable

🔗 AI observability guide →🔗 Production notebook →