Why We Break Things On Purpose: Deterministic Failure Testing

RESEARCH & METHOD

There is a version of AI evaluation that measures how good a model sounds. BigSteve Labs is more interested in a different question: what does the system do when things go wrong? That question has an answer only if you make things go wrong on purpose.

Deterministic failure testing

Deterministic failure-and-recovery testing means the failure is not simulated with a shrug or discovered in production — it is induced, under controlled conditions, at a known point in the run. You choose the fault: a provider rate limit, a quota ceiling, a tool timeout. You choose the moment: 73% into the job, say. Then you watch what the system actually does — and whether what it does is a recovery or a funeral.

The demonstrated Agent Rescue scenario is the template: an AI job fails at 73% due to an HTTP 429 condition; the failure is identified; an alternate provider is selected with human approval; the job resumes from preserved progress; recovery completes. Every step is observable, every decision is on the record, and the whole path can be run again.

Why deterministic beats ‘we tested it’

• Reproducibility — the same fault at the same point yields comparable results across runs, models, and providers.
• Observability — you learn where the system noticed the fault, where it decided, and where it just gave up.
• Honest boundaries — a demonstrated recovery under one condition is evidence for that condition, not a blanket reliability claim.
• Human authority — provider switches and escalations require explicit approval, so recovery never becomes silent drift.

The WebMCP context

This line of reliability and recovery work is built around the WebMCP Challenge — exercising agent workloads through realistic tool and provider conditions, including the failure modes that production systems actually meet. The point is not to win a demo; it is to generate records a serious engineer would accept: what failed, how it was detected, who approved the response, what was preserved, and what completed.

The standard we hold ourselves to

The lab’s experiment standard applies to every run: define the question, isolate the system, record the inputs, preserve the evidence, and publish limitations alongside results. Failures are documented as carefully as successes — a recovery that worked once is labeled once, and a path that failed is published as a failure. That discipline is the difference between reliability engineering and marketing.