LLM observability in practice
Practical writing on agent evaluation, release gating, and building AI features that stay reliable in production.
How to instrument a LangChain agent for production evals
LangChain's callback system gives you hooks into every step. This guide shows how to connect those hooks to Orq so eval scores run automatically on every traced invocation.
Regression testing LLMs is different from regression testing APIs
API tests have a deterministic expected output. LLM output is probabilistic. Here is how to build a regression suite that accounts for output variance.
Building eval suites that actually catch quality drops
A suite with ten metrics feels rigorous but can miss a real regression if thresholds are too loose. How to calibrate your suite against historical data.
Gating LLM releases the same way you gate software releases
Software teams block a merge when tests fail. LLM teams should block a deploy when eval metrics regress. The mechanisms are the same, the tooling just needs wiring.
Traces vs logs: what you actually need for agent observability
Logging every LLM call to stdout gives you data. Tracing gives you the causal structure. The difference matters when an agent run fails after six steps and you need to find which one.
How to measure latency-quality tradeoffs in your eval suite
Switching to a faster model saves cost and latency. Does it also reduce output quality? How to use evals to answer that question before you ship.
Writing an eval rubric for open-ended agent outputs
Deterministic metrics work for factual answers. When your agent writes prose or plans multi-step actions, you need a rubric. Here is a template that works in practice.
Scaling an LLM feature from pilot to production
A pilot with ten users can hide failure modes that appear at a thousand. The observability gaps that kill LLM features at scale and how to close them before you launch.
Structured outputs are not enough for reliable agents
Constrained decoding guarantees your agent returns valid JSON. It does not guarantee the content of that JSON is correct or useful. What else you need.
A minimal eval baseline you can set up in one afternoon
You do not need a perfect eval suite to get value. Three metrics and a working threshold system is enough to catch regressions. A practical starting point.
The real cost of not knowing your agent regressed
A three-week-old regression is not a fresh bug. The damage compounds every day before you find it. How to put a number on the cost of undetected LLM regressions.
Why most teams discover LLM bugs in production
The structural reasons engineering teams find out their AI feature broke when a customer tells them, and the tooling changes that break that pattern.
Put these ideas into practice
Orq gives you traces, eval scores, and CI gating in one place. Free to start, no card required.