Putting an agent in production is harder than a single LLM call, because an agent is a loop with a multiplied failure surface — and you have to both contain it at runtime and measure it over time. A deep-dive on the two disciplines that make production agents trustworthy: guardrails that bound what an agent can do, and evals that test a whole trajectory, not a single output.
You can't assert exact equality on output that's different every time — so how do you test an LLM feature at all? A deep-dive on evals: building a representative test set, scoring properties instead of strings, catching regressions, and treating evaluation as the safety net that lets you change prompts with confidence.
A CI pipeline is only useful if you trust it — and a flaky one is worse than none, because it trains the team to ignore red. A guide to building pipelines that are fast, deterministic, and trustworthy, all the way to deploy.
Cucumber is either a bridge to your domain experts or an expensive layer of indirection — and which one depends entirely on how you write it. Here is where it earns its keep, where it doesn't, and the rules we follow.
Your test suite runs on one core while the other three sit idle. A thorough guide to splitting RSpec across processes with parallel_tests — separate databases, runtime-balanced groups, the isolation bugs it surfaces, and scaling it out across CI nodes.