Know When Your AI Gets Worse, Before Your Customers Do

It’s the work of proving your AI systems work, and keep working. That means evaluation harnesses, agent testing across the whole path an agent takes and not just its final answer, red teaming for how a system breaks, guardrails for hallucinations and unsafe output, and continuous production evaluation so you catch degradation before a customer does. BigStep offers this as its own service because a few of its own agentic builds looked solid in the demo and then got worse once real production traffic hit them.

Your existing QA checks deterministic features against a fixed spec with a pass or fail. AI outputs are probabilistic, so they’re graded against golden datasets and automated scorers instead of matched against one right answer. AI quality engineering also tests an attack surface your current QA process wasn’t built for: prompt injection, jailbreaks, data leakage and tool misuse. BigStep’s AI Reliability Audit applies this to a live system in two weeks and ends with a scored report and a ranked plan for what to fix first.

An evaluation harness is a set of test cases with agreed good answers, automated graders that score what your AI produces, and a way to run all of it every time something changes. It’s wired into your build pipeline, so a new prompt, model or tool gets a score against the baseline before it reaches a user. Without one, whether an AI change made things better is a matter of opinion. BigStep’s Eval Harness Sprint builds one in 4 weeks for $35K to $55K, running in your pipeline with a baseline score for every metric you track.

AI red teaming is deliberately attacking your own AI system to find out how it breaks before someone else does: prompt injection, jailbreak attempts, ways of pulling out data it shouldn’t hand over, and misuse of the tools it has access to. It ends with a report of findings ranked by severity and a plan to fix them. It can be a one-off if you want a point-in-time read without committing to an ongoing programme, or part of continuing evaluation work. BigStep offers it on its own as an AI Red Team Sprint, 2 to 3 weeks and $25K to $45K.

Not from the monitoring you already have. Uptime and error monitoring tell you when something is down, not when answers are slowly getting less accurate, a few points a week, until a customer notices before you do. You need to keep scoring what the AI actually produces in production against the standards you agreed up front, and put those scores on a dashboard so a drop shows up in hours instead of in your support queue weeks later. BigStep builds this as continuous production evaluation and observability, run as a managed service from $5K a month, and its free Agent Reliability Scorecard is a quick first check on where a live system stands today.

Generally, you’ll be asked to show how the system behaves, how its decisions can be traced, and whether it’s been tested for problems like bias, with documentation behind each of those. In practice that tends to mean model cards, decision trails, bias testing results and evaluation evidence, assembled before anyone asks for them instead of scrambled together afterwards. The exact requirements depend on the regulation and your use case, so check them with your legal or compliance team. BigStep builds that evidence and prepares AI systems for EU AI Act and ISO 42001 readiness, starting from a two-week AI Reliability Audit.

Often not yet. If the AI is an internal prototype with a few users and low stakes, a full harness costs more than the mistakes it would catch. It starts paying for itself when real users, real money or a regulator are involved, or when you change models and prompts often enough that you can’t re-check by hand. If you’re not sure which side you’re on, the free Agent Reliability Scorecard scores a live system in about an hour, and it’s fine for the result to be that you don’t need more yet.