Tools to test voice AI agents built on PolyAI cover scenario testing, function-calling accuracy, multi-turn instruction following, telephony, and red-teaming, each with its own tradeoff. Cekura runs all of these against a PolyAI-built agent connected over SIP or a phone number, since no single method catches every failure mode.
TL;DR
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Function-calling accuracy needs its own test path: PolyAI-built agents call backend systems mid-call, and a conversation that sounds right can still call the wrong tool or pass a malformed argument.
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No single test method catches every failure mode: scenario testing catches deterministic flow bugs, persona-varied testing catches accent and interruption failures, telephony testing catches codec and DTMF failures, and each has its own tradeoff in setup cost or coverage.
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Multi-turn instruction-following matters more than single-prompt testing: an agent can follow its rules for the first eight turns and break one on the ninth, per Cekura's instruction-following evaluation guide.
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PolyAI is not one of the 7 configurations in Cekura's benchmark, so no PolyAI-specific pass rate exists; the citable part is the frozen matched study method (7 configurations, 82 scenarios, 3 repeats) (see Cekura Bench).
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Cekura connects a PolyAI-built agent over SIP or a bring-your-own phone number rather than a native integration, then tests it with Mock Tools that stand in for backend systems, so a function call can be checked without touching a live order, refund, or account system.
What is PolyAI, and why does testing it need its own approach?
PolyAI is a fully managed enterprise voice platform built on Raven, a dialog model trained on enterprise contact center conversations specifically, and the difference shows up in fraud detection, multilingual disputes, and multi-turn triage (see Cekura's breakdown of voice AI automation platforms). Testing a voice agent built on PolyAI needs the same discipline any phone-deployed voice agent needs: the conversation has to be tested, and so does everything the conversation triggers, from the specific tool call it makes to the phone line it runs on. Cekura's telephony testing guide makes the general case directly: "Your voice agent works in a browser demo over wideband WebRTC. Real callers connect over narrowband PSTN from cars, parking garages, and noisy rooms." A PolyAI-built agent that passes every test in a clean demo can still fail once a caller's speech and the tools it triggers meet a real phone line.
What tools and methods test a voice AI agent built on PolyAI?
Testing a PolyAI-built agent well means combining methods, since each one catches a different failure mode and costs something different to run. Cekura structures these as separate test types rather than one "does it work" check.
| Method | What it catches | Tradeoff |
|---|---|---|
| 1. Scenario testing (Test Profiles) | Deterministic flow bugs: wrong branch taken, wrong slot filled, wrong step skipped | Catches only what a scenario anticipates; misses failure modes nobody wrote a scenario for |
| 2. Persona-varied testing (Personalities) | Accent, interruption, speaking-speed, and background-noise failures a single well-behaved test caller never triggers | Costs more scenarios per suite to cover the same workflow across caller types |
| 3. Function-calling / tool-accuracy testing (Mock Tools) | Wrong tool called, malformed arguments, or a tool result the agent narrates incorrectly back to the caller | Needs a mocked backend for every tool, or real calls that could touch live systems |
| 4. Multi-turn instruction-following testing | An agent that follows its rules for the first several turns and breaks one later in a long call | Needs full-length multi-turn scenarios, not single-prompt checks, so it costs more to run per test |
| 5. Telephony testing | Codec-related recognition failures, DTMF tones the agent ignores, and SIP or routing failures a browser demo never exercises | Needs real narrowband PSTN test calls, not just clean wideband audio |
| 6. Concurrency and load testing | Latency and error rates climbing under real call volume, even after single-call testing passed | Needs test infrastructure that can place many simultaneous calls |
| 7. Red-teaming | Adversarial callers extracting data or pushing the agent off its rules, with attack success climbing the longer the call runs | Needs multi-turn adversarial scenarios, not a single jailbreak prompt |
Which of the seven to run first depends on where the agent is deployed. Cekura's platform review names fraud detection, multilingual disputes, and multi-turn triage as the flows where Raven's enterprise contact center training shows up (see Cekura's breakdown of voice AI automation platforms), which makes those the scenarios a PolyAI suite covers before a generic happy path.
The seven methods above are not substitutes for each other. A PolyAI-built agent can pass every scenario test and still fail once it calls the wrong tool, and it can call every tool correctly and still drop the call under real network conditions. Cekura runs them as one suite because testing one method and skipping the rest checks a fraction of what production traffic will do to the agent.
How do you connect a PolyAI-built agent to Cekura?
PolyAI ships its own in-environment testing surface for enterprises that want a fully managed deployment "without needing a dedicated engineering team to run it" (see Cekura's breakdown of voice AI automation platforms). Cekura connects a PolyAI-built agent through a platform-agnostic path instead: Cekura's documented native integrations cover Retell, VAPI, ElevenLabs, LiveKit, and Pipecat, with PolyAI absent from that list.
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SIP. Tests SIP-based agents "directly through Cekura's agents, bypassing traditional telephony networks," using a SIP URI such as sip:agent@yourdomain.com (see Cekura's SIP integration docs).
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Custom webhook. Covers agents that expose their own transport, sending call transcripts from a backend system to Cekura via webhook after each call ends (see Cekura's custom integration docs).
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Bring-your-own number. Accepts bring-your-own Twilio phone numbers for inbound and outbound testing (see Cekura's Twilio phone number docs), so a PolyAI-built agent already answering a production number can be dialed as a real inbound caller.
The tradeoff is setup cost: a SIP or custom hookup takes configuration a native integration does not, which is why a PolyAI-built agent is worth connecting once and reused across all seven passes.
How does Cekura test function-calling accuracy for a PolyAI-built agent?
Function calling is where a PolyAI-built agent's conversation turns into an action: an order lookup, a refund, an account change, or another call into a backend system mid-conversation. Cekura tests this with Mock Tools that define exactly what a tool returns for each input, without touching real systems, including testing error responses: what the agent does when a tool returns an unexpected format (see Cekura's scenario testing guide). A test suite has to check both that the agent calls the right tool with the right arguments, and that it narrates the tool's result back to the caller correctly, since those are two separate failure points. Cekura reaches a PolyAI-built agent's tool calls over the same SIP or bring-your-own-number connection that carries the conversation, since Cekura tests SIP-based agents "directly through Cekura's agents, bypassing traditional telephony networks" (see Cekura's SIP integration docs).
How does Cekura test a PolyAI-built agent on the phone line?
A PolyAI-built agent tested only in a browser demo runs on wideband WebRTC audio; a real caller connects over narrowband PSTN audio. Cekura's telephony testing guide documents exactly this gap and what it causes: an agent that confirmed a caller's digits correctly in testing but read them back wrong once the same call dropped to narrowband, a failure clean audio had hidden until the first digit-heavy phone call exposed it. The same guide documents DTMF failing independently of speech: an agent that captured a caller's speech correctly but ignored the keypad tones entirely (see Cekura's telephony testing guide).
Latency has a fixed per-turn budget, and the agent's own processing draws from it: per Cekura's telephony testing guide, the ITU-T G.114 recommendation puts the preferred ceiling for one-way, mouth-to-ear delay at 150ms, a budget the network transport and the STT, LLM, and TTS processing all have to share, quality degrades between 150 and 400ms, and past 400ms a conversation "falls apart." Cekura also tests concurrency directly, with a documented real example of an agent that handled 5 test calls cleanly and degraded badly at 25 concurrent calls, once a provider rate limit started queuing responses.
Does Cekura's benchmark include PolyAI?
No. PolyAI is not one of the 7 configurations Cekura Bench measures, so no PolyAI-specific pass rate exists. Cekura Bench's citable contribution here is its method: a frozen matched study of 7 configurations across 82 scenarios with 3 retained repeats each, where Cekura shared the system prompt, tool definitions, test-case summaries, and test data with every provider, and providers chose their own models, speech components, and settings (see Cekura Bench). Calls that did not connect or produced no transcript stay in the denominator rather than getting dropped from the results, which is the same discipline a PolyAI-built agent needs tested against: a call that never connects is a real production outcome, not a result to discard before scoring. Cekura has separately put a PolyAI-built agent through a complex inbound triage scenario with interruptions and mid-call topic changes, and reported that the agent handled it without the mechanical pauses that surface on other platforms in this category (see Cekura's breakdown of voice AI automation platforms).
How does Cekura test a PolyAI-built agent end to end?
Cekura runs scenario, persona, function-calling, telephony, concurrency, and red-team testing as one suite against a PolyAI-built agent rather than as separate one-off checks: Test Profiles vary caller state, Mock Tools control what backend systems return, and Personalities vary accent, speaking speed, tone, and background noise.
"We are managing thousands of potential conversational paths where a single logic error could result in a failed clinical enrollment. With Cekura, we can now ensure that every new feature makes our agents smarter without compromising on clinical reliability."
Manoj Ananthapadmanabhan, VP Engineering, Twin Health
Test scenarios run against defined pass/fail criteria across metrics including instruction following, latency, CSAT, and interruptions, and Cekura scores instruction following across the full call rather than per turn, since "an agent can follow its rules for the first eight turns and break one on the ninth" (see Cekura's instruction-following evaluation guide). Cekura evaluates 60,000+ voice AI calls daily (see Cekura's automated voice bot testing guide). Teams testing a PolyAI-built agent can auto-generate a first pass of scenarios directly from an agent description and knowledge base, converting them into hundreds of test cases (see Cekura's automated test case generation guide), then move to production monitoring once the agent ships.
Frequently Asked Questions
Does function-calling accuracy need to be tested separately from conversation quality?
Yes. An agent can hold a natural, fluent conversation and still call the wrong tool, pass a malformed argument, or narrate a tool's result back to the caller incorrectly, since these are different failure points. Cekura tests function calling with Mock Tools that define exactly what a tool returns for a given input, so the tool-call path can be checked without touching a live backend system.
Should you trust a voice AI platform's own published latency or accuracy numbers?
Test them independently rather than relying on vendor-published figures alone, since those numbers are typically measured under the vendor's own conditions, not your specific deployment, call volume, or backend integrations. Cekura's benchmark methodology gives every configuration the same system prompt, tool definitions, and test data specifically so results are comparable across platforms rather than self-reported.
What changes when testing a voice agent migrated to or from PolyAI?
A migration to or from PolyAI needs the full test suite re-run, not spot-checked, because the same conversation flow can behave differently once it runs through a different platform's function-calling, latency, and telephony layers. Cekura's automated test case generation can build a first-pass suite directly from the agent's existing description and knowledge base, so a migrated agent starts with hundreds of scenarios rather than zero.
Does multi-agent or multi-workflow setup need more test scenarios?
Yes. Cekura's scenario guidance sizes suites by workflow count: up to 30 scenarios for a single-workflow agent, 30-50 for a medium-complexity agent with 3-5 workflows, and 50-150+ for complex multi-workflow agents, with a practical threshold of 40-60 well-designed scenarios for most medium-complexity agents (see Cekura's scenario testing guide).
How do you test the phone line a PolyAI-built agent runs on?
Test on narrowband PSTN audio, not just the wideband audio a browser demo uses, since the codec change alone can break recognition that worked perfectly in a clean test. Cekura's telephony testing guide documents an agent that correctly confirmed a caller's digits in wideband testing but misread them once the same call dropped to narrowband.
Does red-teaming a voice agent need multiple turns, or does a single adversarial prompt catch the risk?
Multiple turns. Cekura's telephony testing guide notes that adversarial callers behave differently on audio than in chat, and that attack success climbs with turn count, so a single-prompt security test misses most of the risk a full multi-turn adversarial call would catch.
