The Tokenomics of Trust: Evaluate Everything, Not a Sample

Ask most teams how good their AI is and you'll get a confident answer built on a thin slice of the evidence. They evaluate maybe 10% of traffic, often less, and assume the rest behaves the same way. It doesn't. The failures that cost you money and trust are the unusual runs, and a small sample is built to miss them.

There is a reason teams sample, and it isn't laziness. It is cost.

The 10% Problem

The common way to score AI quality is to use a frontier model-as-a-judge. You send the output to a big model and ask it to grade accuracy, safety, or whether the agent did what it was asked. It works. It's also expensive, because the judge costs nearly as much to run as the thing it's judging. Monitoring ends up competing for budget with the workload it's supposed to protect.

So teams ration. They check a sample and hope it represents the whole. The math is upside down. The more traffic you run, the more it costs to evaluate, so the more you skip. Coverage shrinks exactly as the stakes grow.

What a Sample Misses

Averages aren't the problem. The tail is. The runs that blow a budget or embarrass you are rare by nature: an agent that loops on its own work for ten minutes, a prompt-injection attempt that slips through, a tool call that quietly returns garbage. A single agent run can reach thousands of dollars. One firm left usage uncapped and reported a $500 million bill in a single month.

A 10% sample is designed to capture the typical case. It will almost never land on the run that matters, because that run is, by definition, atypical. Sampling tells you how your AI behaves on a normal Tuesday. It says nothing about the Tuesday that ends up in an incident review.

Make Full Coverage Affordable

The fix isn't to evaluate harder. It's to change what evaluation costs.

Small language models built for scoring change the math. Luna-2, the evaluation models from Splunk Agent Observability, powered by Galileo, score quality at roughly 97% lower cost than frontier models. When each eval costs a fraction of a frontier-model judge, the rationing stops. You can score 100% of traffic for accuracy, safety, and the custom quality metrics your business cares about, instead of a sample you cross your fingers over.

That's the line between tooling built to evaluate and tooling with an evaluation feature bolted on. One assumes you'll check everything. The other assumes you'll sample, and prices accordingly.

From Detection to Prevention

Not only are SLM evaluations cheap enough to run on everything, they’re lower latency and can run in real time. And an eval that runs in real time can be used as a guardrail.

That shift matters more than it sounds. Detection tells you what went wrong after a customer already saw it. Prevention stops the output before it ships. Block hallucinated answers, PII leakage, tool misuse, prompt injection, and policy violations at the moment they happen, not in next week's report. The same mechanism catches a runaway agent mid-loop and shuts it down before it becomes a budget story.

Score Everything, and the Patterns Show Up

Full coverage buys more than catching the bad run. Evaluate 100% of traffic and you can watch quality move over time instead of guessing. Ship a new prompt and you see the regression the same day, not after a customer reports it. Swap in a cheaper model and you can prove the quality held, or back it out with evidence. A 10% sample is too noisy to trust for any of that. Full coverage turns quality into a trend line you manage the way you already manage latency and cost.

Where Most Tools Leave a Gap

Two kinds of tools claim this space, and both leave a gap. AI-native eval and tracing tools go deep on the LLM application layer, which is the right instinct, but they stop there. They don't get visibility into the GPUs, memory, vector databases, network, and other infrastructure the workload runs on, and they offer little for the open-weight, on-premises infrastructure enterprises are now standing up.

General observability platforms have the opposite gap. Broad coverage of services and infrastructure, with evaluation treated as an add-on. They can tell you an agent is slow. They struggle to tell you it's wrong.

One Platform Built To Evaluate the Entire AI Stack

Splunk Agent Observability treats evaluation as the core job, not a feature. Luna-2 models score 100% of traffic affordably, and one dashboard shows every agent's requests, latency, tokens, and quality side by side.

It also connects that quality to the hardware underneath. Token cost and behavior tie back to the GPUs, memory, and vector databases running the workload, across Cisco AI PODs, NVIDIA NIMs, and Milvus. You're not just grading the model. You're seeing what it costs to run and whether the result was worth it, in one view. Runtime guardrails powered by the same Luna models enforce quality and safety as it happens.

Stop Sampling

Monitoring should cost a sliver of what you spend running the model. When it does, the whole calculus changes. You stop guessing from 10% and start seeing all of it, which is the only way to catch the runs that actually cost you.

Trust isn't a sample. It's the evidence that every run, not just a lucky tenth, did what it was supposed to. Score everything and you can prove it.

FAQ