OpenTelemetry
5 posts tagged #opentelemetry
5 posts on OpenTelemetry, 3 of them in the AI Agent Architecture & Frameworks pillar, with 2 in Production AI Engineering — most read: “Execution Trace Tree for AI Agents: Build One in 60 Minutes” and “How to Build Vendor-Neutral LLM Observability”.
AI and Machine Learning Execution Trace Tree for AI Agents: Build One in 60 Minutes
Stop drowning in agent logs. Instrument a deterministic execution trace tree (spans, tool calls, checkpoints) so you can replay failures and diff runs like real engineering.
Cloud and DevOps How to Build Vendor-Neutral LLM Observability
A practical blueprint for vendor-neutral LLM observability: an OpenTelemetry pipeline, an open trace schema, redaction boundaries, tail sampling, and cost controls that survive vendor swaps.
Cloud and DevOps How to Pick LLM Application Observability Metrics
Token logs are table stakes. Here’s the minimum set of LLM application observability metrics for tools, RAG, caching, refusals, and privacy-safe logging that actually debugs production incidents.
AI and Machine Learning AI Agent Observability Logging Schema: OTel + Redaction
A copy‑paste JSON logging contract for agent runs (spans, tool I/O, cost, retries) plus a minimal OpenTelemetry mapping you can implement in a weekend—without leaking secrets.
![developer monitor terminal logs tracing — illustration for article on OpenTelemetry Instrumentation for AI Agents [2026]:](https://img.kunalganglani.com/images/vzekdneq/production/f126a9eb93656ea60adeae440cf4b74ed0b5fb93-1200x675.webp?w=600&q=75&auto=format)
AI and Machine Learning OpenTelemetry for AI Agents: Tracing Schema and Spans
A vendor-neutral tracing schema for AI agents: model LLM calls, retrieval, tool runs, retries, and token cost as spans. Then dashboard latency, error tax, and cost per successful task.
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