#observability
11 posts tagged with #observability
Every article below is hand-written, technically reviewed, and focused on observability. 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 How to Use MLflow LLM Evaluation Tracing [2026] (Spans → Gates)
Turn MLflow traces into living eval datasets, rubric scorecards, and CI/CD regression gates for tool-calling agents. Stop debugging with random logs.
AI and Machine Learning How to Ship OpenAI Agents API Guardrails in 1 Day [2026]
Production tool-use fails on governance, not prompts. Here’s a practical setup for OpenAI Agents API guardrails: allowlists, layered rate limits, and audit logs you can actually use in incidents.
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.
Cloud and DevOps 7-Step Plan: eBPF Observability Without Sidecars on Kubernetes
A pragmatic migration plan for Kubernetes teams moving from Envoy sidecars to node-level eBPF observability, with P99 validation and telemetry cost controls baked in.
Frontend and Mobile Debug HTTP/3 QUIC in Production: 8-Step Playbook [2026]
A Wireshark-free workflow to prove HTTP/3 is actually negotiated, diagnose silent fallback to HTTP/2, and measure real perf impact using DevTools, curl, and logs.
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.
Cybersecurity Prevent Sensitive Data Leakage in RAG: The 2026 Playbook
RAG leaks rarely happen in the model. They happen in logs, traces, and vector stores. Here’s a practical 2026 playbook to ship redaction, least-context retrieval, and auditable controls end-to-end.
![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.
Cloud and DevOps eBPF Monitoring Is Replacing Sidecars: How Datadog Proved the Cloud-Native Future [2026]
Sidecar proxies had a good run, but eBPF is eating the service mesh from the kernel up. Here's how Datadog's bet on kernel-level observability is reshaping cloud-native monitoring.