In the fast-moving world of conversational AI, it's tempting to think that "no news is good news." If your voice or chat agents aren't throwing errors or crashing, they're probably doing fine, right?
Unfortunately, that assumption is costing companies more than they realize.
At Cekura, we work with teams deploying some of the most advanced voice and text-based AI agents in the world. And what we've found across verticals like fintech, e-commerce, and healthcare, is that the most dangerous failures don't break your systems. They quietly break your user experience, your ops efficiency, and your bottom line.
Let's talk about the hidden cost of "mostly working."
The Silent Nature of LLM Failures
Unlike traditional software, LLM-based agents often fail silently. There's no 500 error. No crash report. No alert.
Instead, these issues creep in subtly:
- A voice agent takes 5 seconds too long to respond.
- A chatbot defaults to generic fallback responses more often than usual.
- An agent misinterprets intent and loops the user through a useless flow.
You don't catch these in QA, and users don't always report them: they just leave. Or worse, they call your support line, frustrated.
The Compounding Impact
We've seen how these seemingly small issues compound over time:
Rising support costs: More customers escalate issues the bot should've resolved.
Sales inefficiency: Voice agents mishandle qualification questions, leading to bad leads or missed opportunities.
Misleading analytics: Your dashboards show high "intent matched" rates, but users are still dropping off early.
Churn and abandonment: Customers walk away after just one poor interaction—even if the system was "technically working."
A 10% increase in fallback rate might not seem urgent. But at scale, it can mean millions in lost revenue or added labor costs.
The Detection Problem
One of the hardest parts? Most standard analytics tools won't flag these issues. Logs and transcripts might show a "resolved" session, even if the agent misunderstood the user.
And traditional QA or human review doesn't scale. You'd need to comb through thousands of interactions a week to catch these subtle shifts.
How Cekura Helps
At Cekura, we help AI teams continuously monitor and evaluate LLM-based agents—so these quiet failures don't go unnoticed.
Our platform:
- Detects early signals of failure like excessive fallback use, long latency, intent mismatches, and looping
- Sends real-time alerts when agent performance dips below threshold
- Offers trend analysis across time, regions, use cases, or agent versions
- Helps you benchmark performance across different AI vendors or internal models
In short: We help you find problems before your users do.
The Need for Continuous Evaluation
LLM agents constantly evolve. Model updates, data drift, prompt tweaks, user behavior: these all influence how your agent performs.
That's why one-time QA isn't enough. Continuous evaluation is the new ops must-have.
By catching and resolving issues early, you're not just improving accuracy, you are protecting revenue, customer satisfaction, and brand trust.
Stop Normalizing Silent Failure
If you're running an AI assistant and haven't looked at its failures lately, now's the time. Because "good enough" isn't good enough anymore.
Let's stop normalizing silent failure—and start building voice and chat agents that actually perform.
If you are interested in evaluating your agents proactively, reach out to us, we would love to show you how we help.