Observability

TLDR: LLM observability has become mission-critical infrastructure for teams shipping AI applications to production. This guide evaluates the top five LLM observability platforms heading into 2026: Maxim AI, Arize AI (Phoenix), LangSmith, Langfuse, and Langsmith. Each platform is assessed across key dimensions including tracing capabilities, evaluation workflows, integrations, enterprise readiness,

Compare the best LLM observability platforms for 2026 across distributed tracing, evaluation, and production monitoring, with Maxim AI as the leading choice. LLM applications fail in ways traditional monitoring tools were never designed to catch. A correctly returned 200 response can still contain a hallucination, a drifted tone, a leaked

A practitioner's guide to monitoring LLM latency and cost in production, covering trace-level observability, tail percentiles, token accounting, semantic caching, and gateway-level governance. TLDR User experience and unit economics in production AI hinge on LLM latency and cost. Effective monitoring rests on end-to-end traces, P95/P99 tail control,

AI agents now make thousands of autonomous decisions daily across customer support, code generation, healthcare triage, and financial operations. Unlike traditional chatbots that return a single response, agents break problems into multi-step reasoning chains, invoke external tools, retrieve context, and sequence actions to accomplish goals. When something goes wrong, traditional

A practical guide to the seven core metrics for AI agent observability, covering reliability, debugging speed, cost control, and production-grade performance. TL;DR This article covers seven core metrics worth tracking for AI agent observability: Step Completion, Step Utility, Task Success, Tool Selection, Toxicity, Faithfulness, and Context Relevance, applied at