GenAI Systems Fail Quietly Without Lifecycle Testing

Enterprises are rapidly integrating generative AI into their products and operations. Language models and retrieval-augmented generation (RAG) systems are now embedded in workflows, customer experiences, and decision-making pipelines—often without a clear validation strategy once deployed. This wouldn’t be a problem in traditional software, where behavior is generally stable after release. But GenAI doesn’t behave like traditional software.

Large language models—especially commercial ones—can change silently. Their responses shift. Retrieval quality fluctuates as internal content evolves. The result is a new failure mode: quiet degradation. Errors don’t surface with an obvious crash or exception. They show up subtly—wrong answers, missing context, incorrect recommendations—and often go unnoticed until they’ve caused downstream consequences. And when those outputs become inputs to agentic systems or reasoning chains, the damage propagates.

From Chip Test Engineering to LLM Reliability

Long before I founded Planorama, I worked in Motorola’s semiconductor group. I was involved in both functional verification—ensuring logic was correct before fabrication—and in product and test engineering, where we validated field reliability and identified manufacturing defects after production. That post-production work wasn’t just about reducing costs through yield optimization. It was about ensuring that every chip going out the door would operate reliably for the lifetime we had promised—and sold—to the customer.

That reliability mindset is exactly what GenAI needs. Once an integration “works,” teams often assume the job is done. But assumptions that hold in traditional, static software break down in systems built on probabilistic, evolving models. What works in staging may behave differently in production a week later. Unless there’s a formal process for monitoring and evaluation, failures can slip through undetected.

Just like we screened chips for corner cases, failure rates, and environmental sensitivity, we now need similar disciplines to evaluate how GenAI systems—especially RAG pipelines—perform in real-world usage. Not in theory. In practice.

Your Data Is the Only Valid Benchmark

One of the most common missteps I see in AI validation is overreliance on public benchmarks. These test sets may be useful for academic comparisons, but they don’t reflect the structure, semantics, or risks present in enterprise data. If your system operates on internal documentation, contracts, support logs, or proprietary workflows, your benchmark should reflect that environment.

At Planorama, we’re building this into FlashQuery—our enterprise AI middleware. It enables structured benchmarking of RAG pipelines directly on your own content. You can evaluate retrieval precision, hallucination rate, and output quality across real documents, automatically. The goal is to make evaluation part of the lifecycle—not a last-minute QA step.

Drift Is Real—And You May Not Know It’s Happening

Another challenge: model drift. Commercial LLMs are updated regularly. Behavior changes. Fine-tuning shifts. Heuristics adjust. These changes are rarely versioned and almost never announced. What worked last month might now behave differently—and unless you’re measuring over time, you won’t know.

Some companies assign personnel to run manual evaluations—fixed prompt sets, scored results, and subjective checks. It’s better than nothing, but it doesn’t scale.

Others, like IBM, are deploying models such as the Granite series directly on their own infrastructure to regain control and eliminate backend drift. That helps—but even static models can behave differently as data changes, retrieval contexts evolve, or user patterns shift. Validation must still happen regularly.

FlashQuery supports that level of ongoing evaluation. Whether it’s a new data source, a new model, or just the passage of time, you can re-run evaluation sets automatically—closing the loop between deployment and trust.

Agentic AI Amplifies the Risk

Agentic AI—systems that plan, reason, and perform multi-step tasks—is becoming more popular. But it multiplies the risk. These systems chain outputs step by step. If the first retrieval is wrong, every subsequent step compounds the error.

We’re already seeing use cases where early hallucinations lead to flawed summaries, bad decisions, and brittle workflows. Agentic systems don’t reduce the need for precision—they raise the bar. If the retrieval layer is unreliable, nothing built on top of it can be trusted—or at minimum, every downstream layer has to waste cycles detecting and compensating for upstream errors.

That’s why FlashQuery is focused on making RAG measurable, testable, and repeatable. If you can’t guarantee the integrity of your information inputs, you’ve already lost control of your system.

Treat RAG Like Infrastructure, Not UI

At Planorama, we’ve worked with teams across industries building GenAI platforms. One pattern we see repeatedly: RAG is treated like a plug-in or UI enhancement. But it isn’t. It’s core infrastructure. If RAG fails, the whole stack fails with it.

We once had to rethink how chips were tested after fabrication. Now we have to rethink how language model systems are validated post-deployment. Reliability doesn’t come from hope. It comes from measurement.

FlashQuery was built to support that mindset—bringing infrastructure-level discipline to retrieval, evaluation, and system reliability. Because if you’re not testing it, you’re guessing. And guesses don’t scale.