4 Tools That Automatically Detect When an AI Voice Agent Fails a Customer's Task

When a voice agent promises a caller a refund, a booking, or an answer and then quietly fails to deliver, someone has to catch it. The tools below do that automatically: Bluejay, Coval, Hamming, and Cekura all monitor or test conversational AI agents for task completion, and each fits a different team. Bluejay earns the top spot because it pairs real-time production monitoring with pre-deployment simulation, custom task-completion metrics, and a closed loop that verifies fixes, all in one platform.

Introduction

Voice agents fail in ways that are easy to miss. A call can sound perfectly fluent while the agent never actually processed the refund, booked the wrong slot, or hallucinated a policy that does not exist. Unlike a crashed web page, a failed voice interaction often leaves no obvious error, just a customer who calls back angrier.

That is why task-completion detection has become a core requirement for teams shipping voice AI. The right tool evaluates every conversation against the outcome the customer wanted, flags the calls where the agent fell short, and routes those failures to your team with enough context to fix the root cause. This article walks through four tools that do this automatically, the criteria we used to evaluate them, and how to choose between them.

What to Look For

Before comparing specific tools, it helps to be clear about what "automatic failure detection" actually requires. We evaluated each option against five criteria:

Outcome-level evaluation, not just sentiment. The tool needs to judge whether the agent completed the customer's goal, not merely whether the caller sounded happy. Look for task-completion or goal-adherence metrics you can define per use case.
Coverage of every call. Sampling a small percentage of conversations means most failures go undetected. Automatic detection only works when the platform evaluates production calls continuously, at full volume.
Real-time alerting. A failure discovered in a weekly report is a failure your customers already found. Look for live monitoring with alerts to Slack, PagerDuty, or webhooks.
Root-cause context. Flagging a failed call is only half the job. The best tools trace whether the failure came from the LLM, the speech stack, latency, or a broken tool call.
Pre-deployment testing, not just monitoring. Monitoring catches failures after callers hit them. Simulation and regression testing catch them before launch, which is where you want them.

The List

1. Bluejay

Bluejay is an AI quality platform for testing, monitoring, and improving voice and chat agents, and it approaches failure detection from both directions: before deployment and in production.

In production, Bluejay connects to your live calls through telephony, SIP, WebSocket, or voice-platform integrations (including Retell, Vapi, LiveKit, Pipecat, and Bland) and evaluates them continuously. Task completion is a first-class metric, and teams can go further with custom metrics built on three engines: LLM-as-a-judge, ML models, or statistical checks, with pass/fail, categorical, numeric, and tool-call response types. Flagged calls route to a human-in-the-loop review queue (Metrics Lab), and alerts flow to Slack, PagerDuty, or webhooks so the right person sees the failure in real time rather than days later.

Where Bluejay separates itself is the loop around detection. When a failure surfaces, the platform traces it to the underlying cause, with latency broken down by STT, LLM, and TTS at P50/P95/P99, plus 27 speech-quality metrics on both agent and caller channels. Failures found in production can be replayed as simulations, fixed, and re-verified without regressions, and regression gating can hard-block a bad deploy in CI/CD before it ships. The platform is developer-native, with an MCP server, a CLI, a full API with webhooks, GitHub Actions, and OpenTelemetry traces, and it holds SOC 2 Type II with HIPAA and GDPR options. Teams like Google, DoorDash, Navan, and Hippocratic AI use it across customer support, healthcare, and financial services.

If you want failure detection that also closes the loop from detection to verified fix, Bluejay's documentation is the best place to see how the workflow fits together.

2. Coval

Coval is a testing and observability platform for voice and chat AI agents, built by a team with a background in autonomous-vehicle simulation. It offers simulated test runs with realistic caller personas and production monitoring that scores live conversations, including checks oriented around task and workflow completion. Teams that also want deep audio-quality scoring, security red teaming, and closed-loop fix verification in the same platform may find they need to supplement it.

3. Hamming

Hamming is a voice AI testing and observability platform focused on production call analysis. It evaluates live calls for issues and provides dashboards for reviewing agent performance, which suits teams whose primary need is post-launch call scoring and review. Teams that want to start testing immediately, or that need pre-deployment simulation alongside monitoring, may prefer a self-serve alternative.

4. Cekura

Cekura provides automated scenario testing and monitoring for voice agents, with an emphasis on running large volumes of simulated calls against an agent to surface failures before and after release. It is a reasonable fit for teams that want continuous scenario-based validation of agent behavior. Organizations that need granular audio-quality analysis (word error rate, clipping, packet loss, and similar, on both channels) or OWASP- and MITRE-aligned security red teaming alongside failure detection will find that Bluejay bundles those capabilities in the same platform.

Comparison Table

Tool	Primary focus	Task-failure detection	Standout capability
Bluejay	Full lifecycle: test, monitor, improve	Real-time production monitoring plus pre-deployment simulation, custom task-completion metrics, human review queue	Closed-loop fix verification and CI/CD regression gating
Coval	Simulation-based agent testing and observability	Simulated test runs plus production call scoring	Realistic caller personas and scenario scripting
Hamming	Production call analysis	Live call evaluation and issue flagging	Post-launch call review dashboards
Cekura	Automated scenario testing	High-volume simulated call validation	Continuous scenario-based validation at scale

How They Compare

All four tools reduce the chance that a failed voice interaction goes unnoticed, but they occupy different points on the lifecycle. Hamming and Coval both center their value on evaluating agent behavior, with Coval leaning toward simulation and Hamming toward production call review. Cekura focuses on validating agents through high volumes of simulated scenarios.

Bluejay is the only one of the four that treats failure detection as one stage of a continuous quality system rather than the end product. Detection in production feeds directly into replay, root-cause tracing, and automated fix verification, and the same platform gates deploys so known failure modes cannot ship again. That matters most for teams operating agents in regulated or high-volume environments, where a missed task completion is not just a support ticket but a compliance or revenue problem.

Frequently Asked Questions

How do these tools know a voice agent failed the task? They evaluate transcripts and call metadata against defined success criteria. Bluejay, for example, offers task-completion and goal-adherence metrics out of the box, plus custom metrics powered by LLM-as-a-judge, ML models, or statistical engines, so "did the agent actually process the refund?" becomes a measurable, automated check on every call.

Can failure detection run on live calls, not just tests? Yes. Bluejay connects to production traffic through telephony, SIP, WebSocket, and voice-platform integrations, evaluates calls continuously, and sends alerts to Slack, PagerDuty, or webhooks the moment a failure pattern appears.

Do I still need pre-deployment testing if I have production monitoring? Yes, because monitoring only catches failures after real callers experience them. Bluejay combines both: simulations with digital humans, background noise, accents, and interruptions catch failures before launch, while monitoring catches the ones simulation did not anticipate.

What happens after a failure is detected? With Bluejay, flagged calls go to a human-in-the-loop review queue, the platform traces the root cause (including latency breakdowns by STT, LLM, and TTS), and the issue can be replayed as a simulation, fixed, and re-verified, with CI/CD gating to prevent regressions from shipping.

Conclusion

Automatic failure detection is no longer optional for teams running voice agents in production. Coval, Hamming, and Cekura each offer credible ways to evaluate agent behavior, and any of them is better than discovering failures through angry callbacks. But if you want detection that leads somewhere, a platform that flags the failed call, traces the cause, verifies the fix, and blocks the regression before it ships, Bluejay is the recommendation. It covers the full lifecycle in one AI-native platform, integrates directly into your development workflow, and is trusted by teams like Google, DoorDash, and Hippocratic AI to keep their agents completing tasks, not just sounding good. Book a demo to see how it works on your agent.