#tracing
1 resource and 8 guides tagged #tracing on ChangeGamer.
- Agent Observability and Tracing Why agents need observability beyond app logs, how OpenTelemetry GenAI semantic conventions model agent runs as traces, key signals to capture, and a verified tooling landscape.
Guides
- MCP Server Observability with OpenTelemetry: Spans, Metrics, and Trace Correlation Instrumenting an MCP server past the pillar's baseline: what to put on a tool-call span beyond gen_ai.tool.name, what replaces the deprecated Logging primitive in practice, per-tool-name latency and error-rate metrics, and how a trace ID actually survives the agent-to-upstream-API hop.
- What Should an AI Agent's Observability System Capture? An operator playbook for instrumenting an AI agent: the trace/span model behind a run, the OpenTelemetry GenAI attributes that name each field, the fields worth capturing per span, and what to redact before any of it gets logged.
- AI Agent Observability and the Production Evaluation Playbook AI agent observability and evaluation once an agent is already live: tracing spans, retrieval-attribution logs, judge-based screening vs. human acceptance, online metrics, cost telemetry, and incident postmortems that feed evaluation fixtures.
- Distributed Tracing for Multi-Agent AI Systems How trace ID propagation actually works across a multi-agent handoff, why handoff and delegation need different trace shapes, and why an orchestrator-level trace can hide a failed leg of a fan-out.
- How to Sample and Retain Production AI Agent Traces How to sample and retain production AI agent traces: head vs tail sampling, keep-all-errors plus a random baseline, whole-trace decisions for multi-agent runs, retention tiers and redaction before the clock starts.
- How to Keep Trace Data When an AI Agent Crashes Mid-Run How to keep trace data when an AI agent crashes mid-run: write a small run-start record outside the trace buffer, detect orphans, count them as unknown outcomes, and force-decide on shutdown.
- How to Choose an LLM Observability Platform for AI Agents How to choose an LLM observability platform: decide on OTel-native ingestion, self-host versus cloud, export portability, redaction hooks and retention support before comparing vendors.
- How to Redact PII From AI Agent Traces: Placement, Testing and Cleanup How to redact PII from AI agent traces in practice: where the redactor sits in the pipeline, what to do per span field, how to test it with seeded fake PII, and how to clean up after a leak.