Here’s a comment I make in customer conversations that usually gets a double take: AI agents don’t just get slower under load. They behave differently.
We’ve load tested software for decades, and the question was always the same: does the system stay up and stay fast when traffic spikes? Necessary, but for agentic bots it’s not sufficient, because the failure mode under load isn’t a 500 error. It’s a wrong answer, delivered fluently and confidently, with a 200 status code.
Three things are going on.
First, load literally changes what the model says. Under high traffic, inference servers process many requests together in larger batches. A side effect is that the model’s internal calculations come out slightly different. That sounds too small to matter, but the model picks its next token from those numbers, and slightly different math can tip it to a different token. One different token early in a response steers everything after it. Engineers will recognize the behavior: it looks like a race condition, concurrency-dependent and hard to reproduce. The practical implication is uncomfortable. An agent validated at low concurrency has never been tested on the outputs it will produce at peak. Same prompt, different answer, purely as a function of who else is talking to the system at that moment.
Second, an agent’s grounding erodes under load, and it erodes silently. Accuracy depends on a chain of dependencies: knowledge retrieval, API calls, context assembly. Under load these time out, return partial results, or get truncated. The model doesn’t throw an error when that happens. It answers anyway, without grounding. That is what hallucination looks like in production, and load is exactly when it spikes.
Third, agents compound their errors. These aren’t single question-and-answer exchanges. An agent plans, calls tools, and acts across many steps to complete a task, so a degradation that looks marginal at the component level adds up: a 5% per-step error rate across a six-step journey means roughly a quarter of those journeys go wrong end to end. And in a live conversation, latency itself is a functional failure. Three seconds of silence on a voice call causes talk-over and abandonment; timeouts in chat trigger retries and duplicate actions.
None of this shows up on an infrastructure dashboard. Everything stays green while the agent confidently gets it wrong, because nothing failed in a way infrastructure can see.
The net: correctness under load is an assurance problem, not a performance problem. The bar for load testing agentic bots is running real conversations at production concurrency and scoring whether the agent still achieved the customer’s intent, correctly and in bounds. And because AI is never perfect, pre-launch proof has to pair with live observability and governance once the agent is in front of customers. One continuous discipline, not a phase.
This is the exact problem we’re solving at Cyara. If you’re putting AI agents in front of customers, get in touch, happy to compare notes.
