#opentelemetry
5 posts tagged with #opentelemetry
Every article below is hand-written, technically reviewed, and focused on opentelemetry. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
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 Monitoring [2026]
A practical blueprint for llm observability monitoring vendor neutral: 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 [2026]
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 [2026]: 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?auto=format&fit=max&q=75&w=600)
AI and Machine Learning OpenTelemetry Instrumentation for AI Agents [2026]: Ship It
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.