PolyAI ships native testing through its Agent Development Kit, and Cekura tests the same agent independently over SIP or a phone number. Together they cover scenario simulation, function-calling accuracy, telephony behavior, concurrency, red-teaming, and production monitoring, because no single method catches every way a voice agent built on PolyAI fails in production.
TL;DR
- PolyAI is not a platform without tests: it ships simulation tests, A/B testing, and CI gating through the ADK CLI and API, and those run on PolyAI agents, per Cekura's PolyAI pricing breakdown.
- A vendor's own test suite cannot tell you how that vendor compares to the alternative, which is the specific gap an independent platform such as Cekura fills during a bake-off or a migration.
- ADK agent resources are YAML and Python files that teams branch, review, and merge like application code, so every merge is a change that deserves a regression run rather than a spot check.
- Raven is the default model on PolyAI with GPT-5, Claude, and Gemini available as swaps, and a model swap changes the conversation without changing the flow, so the suite has to score behavior rather than structure.
- PolyAI is not one of the 7 configurations in Cekura Bench, so no PolyAI pass rate exists anywhere; the citable part is the frozen method, 7 configurations across 82 scenarios with 3 retained repeats.
What does a PolyAI-built agent do that changes how you test it?
PolyAI is a fully managed enterprise voice platform built on Raven, a dialog model trained on enterprise contact center conversations, and Cekura's review of voice AI automation platforms reports that the training shows up in fraud detection, multilingual disputes, and multi-turn triage. Three properties of the platform change what a test suite has to cover. Agents are built in Agent Builder or through the Agent Development Kit, and ADK resources are YAML and Python files that teams branch, review, and merge like application code, which makes every merge a change worth a regression run. Raven runs as the default model with GPT-5, Claude, and Gemini available as swaps, so the model underneath a conversation can change while the flow stays identical. And the deployment is managed, which puts integrations such as Custom SIP on PolyAI's side of the line rather than yours. Cekura tests the agent that comes out of all three, from the outside, on the connection a real caller dials.
What are the most effective automated testing tools for voice AI agents built on PolyAI?
There are two categories of tool, not one, and they answer different questions. Platform-native testing answers whether this build regressed against the last build, and it runs inside the platform that shipped it. Independent testing answers whether the agent is good enough at all, and how it compares to an agent built somewhere else. Cekura's PolyAI pricing breakdown puts the split plainly: PolyAI runs the agent, and Cekura tests it, including agents running on someone else's platform. The table below reads as a suite rather than a shortlist, because the effective answer is a combination and not a single product.
| Tool | Where it runs | What it answers | Tradeoff |
|---|---|---|---|
| PolyAI simulation tests, A/B testing, and CI gating through the ADK CLI and API | Inside PolyAI, against ADK resources | Did this merge regress the flows, prompts, and resources we control? | Scoped to PolyAI agents, so it cannot score the same scenarios against an agent built on another platform |
| Cekura scenario and persona testing | External, over SIP or a phone number | Does the agent hold up across caller states, accents, interruptions, and background noise? | Needs a connection configured once, which a platform-native suite does not |
| Cekura Mock Tools | External, standing in for backend systems | Did the agent call the right tool with the right arguments, and narrate the result correctly? | Needs a mock defined per tool, or live calls that touch real order and account systems |
| Cekura telephony and concurrency testing | Real narrowband PSTN and SIP legs | Does it survive the codec, DTMF, and call volume a browser demo never applies? | Costs real test calls rather than simulated audio |
| Cekura multi-turn red-teaming | External, adversarial scenarios | Does the agent hold its rules across a long call, not just one prompt? | Needs full-length adversarial conversations, which cost more per run |
| Manual call-throughs | A human on a phone | Anything a written scenario failed to anticipate | Does not scale, is not repeatable, and cannot be gated in CI |
The rows are not substitutes for one another. A PolyAI-built agent can pass every native regression test and still call the wrong backend tool, and it can call every tool correctly and still lose digits once the call drops to narrowband. Picking one row and skipping the rest checks a fraction of what production traffic does to the agent.
What is the top rated software for conducting end-to-end testing on PolyAI conversational agents?
End to end means the whole lifecycle, not a longer scenario. Cekura's PolyAI pricing breakdown sets out three stages, and a suite covering one of them is a partial answer. Cekura simulates hundreds of caller scenarios with custom personas and accents before a number goes live, then red-teams it against jailbreaks and adversarial callers. Cekura then tests interruptions, background noise, and latency under concurrent load, where per-minute costs and containment rates both move. Cekura then monitors live conversations for instruction-following, tool-call accuracy, and CSAT, with Slack alerts on latency spikes and quality drops.
| Stage | What Cekura runs | Why a PolyAI-built agent needs it |
|---|---|---|
| Pre-production | Persona-varied scenarios, mocked tools, red-team runs | Catches flow and tool failures before a managed deployment answers a real number |
| Infrastructure | Interruptions, background noise, concurrent-load latency | Managed hosting does not exempt the agent from carrier conditions or call volume |
| Observability | Live scoring for instruction-following, tool-call accuracy, CSAT | A frozen test suite stops learning the day real traffic starts arriving |
Running all three against one harness makes a vendor comparison reproducible rather than anecdotal.
"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
Thousands of reachable paths cannot be signed off by sampling a few by hand.
How do you connect a PolyAI-built agent to Cekura?
Cekura's documented native integrations cover Retell, Vapi, ElevenLabs, LiveKit, Pipecat, and Bland, and PolyAI is not on that list, so a PolyAI-built agent connects through a platform-agnostic path instead. There are three, depending on what the agent already exposes.
- SIP. Cekura tests SIP-based agents "directly through Cekura's agents, bypassing traditional telephony networks", taking a SIP URI in the form
sip:agent@yourdomain.com, per Cekura's SIP integration docs. The documented flow is outbound: Cekura sends SIP INVITE requests to an endpoint you configure. - Bring your own phone number. Cekura accepts imported Twilio numbers "for inbound and outbound testing", per Cekura's Twilio phone number docs, so an agent already answering a production number can be dialed as a real inbound caller.
- Custom webhook. Cekura's custom integration docs cover agents that expose their own transport, with a backend system posting call transcripts to Cekura after each call ends.
The configuration cost is paid once, then reused across every scenario, telephony, load, and red-team pass.
How do you test function-calling accuracy on a PolyAI-built agent?
Function calling is where a PolyAI-built agent stops talking and starts acting: an order lookup, a refund, an account change, or another call into a backend system mid-conversation. Cekura tests this with Mock Tools, which "let you define exactly what the tool returns for each input" without touching real systems, per Cekura's scenario testing guide. Two separate failures live here and a suite has to score both. The agent can call the wrong tool or pass a malformed argument, and it can receive a correct tool result and narrate it back to the caller wrongly. Error paths matter as much as happy paths, so the same guide covers what the agent does when a tool returns an unexpected format. Cekura reaches those tool calls over the same SIP or phone-number connection that carries the conversation, which means function-calling accuracy is scored on the transport a real caller uses rather than inside an isolated harness.
How does a PolyAI-built agent hold up on a real phone line?
A managed deployment does not change physics. Cekura's telephony testing guide states the gap 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." The same guide records an agent that confirmed "1-5-1-3" as "1-9-1-3" once the call dropped to narrowband, hidden by clean audio until the first digit-heavy call. DTMF fails independently of speech, so keypad entry needs its own scenarios. Latency carries a fixed budget: per that same guide, the ITU-T G.114 recommendation puts the preferred ceiling for mouth-to-ear delay at 150 ms, shared by the network transport and the STT, LLM, and TTS processing, quality degrades between 150 and 400 ms, and past 400 ms a conversation falls apart. The guide also records an agent that handled 5 test calls cleanly and degraded badly at 25 concurrent calls once a provider rate limit began queuing responses.
What are the comprehensive QA and monitoring tools specifically for voice AI agents built on PolyAI?
A test suite is frozen and production is not, so QA on a PolyAI-built agent splits into a pre-launch half and a continuous half that runs after launch. Cekura monitors live conversations for instruction-following, tool-call accuracy, and CSAT, with Slack alerts on latency spikes and quality drops, and it does that over the same SIP or phone-number connection that carried the pre-launch tests, so nothing has to be rebuilt at launch. Instruction-following is the metric that decays quietly: an agent can respect its rules for eight turns and break one on the ninth, which single-turn scoring never sees. Adversarial pressure behaves the same way. Cekura's multi-turn red-teaming write-up reports that attack success climbs with the number of turns, and the Crescendo attack described by Russinovich, Salem and Eldan works by escalating a dialogue gradually while referencing the model's own replies. Monitoring that scores whole conversations rather than single turns is what catches both of these.
Does Cekura's benchmark include a PolyAI configuration?
No. PolyAI is not one of the 7 configurations in Cekura Bench, so no PolyAI-specific pass rate or response time exists to quote, and any figure presented as one is invented. What Cekura Bench contributes here is method. It is a frozen matched study, "7 configurations, 82 scenarios, 3 retained repeats", where Cekura shared the system prompt, tool definitions, test-case summaries, and test data with every provider, and "calls that did not connect or produced no transcript stay in the denominator", per Cekura Bench. Two caveats travel with any number lifted from it. Providers chose the configuration they wanted benchmarked, with OpenAI the exception, tested directly by Cekura on gpt-realtime-2.1 with no provider-submitted configuration. And response time there is Cekura's main-agent measure, not provider-reported STT, LLM, or TTS timing. Leaving failed connections in the denominator is the discipline a PolyAI suite should copy: a call that never connects is a production outcome, not a result to drop before scoring.
FAQ
Does PolyAI ship its own testing tools?
Yes. PolyAI ships simulation tests, A/B testing, and CI gating through the ADK CLI and API, per Cekura's PolyAI pricing breakdown, and those run against ADK agent resources held as YAML and Python files. They answer whether a build regressed. They do not score the agent against one built on a different platform, which is the question a bake-off or a migration actually asks.
Do you still need independent testing if PolyAI manages the deployment?
Yes, for two reasons. A managed deployment still runs over a carrier, so narrowband codecs, DTMF handling, and concurrency limits apply exactly as they do anywhere else. And a vendor's own test suite cannot tell you how that vendor compares to the alternative, so any evaluation between PolyAI and a second stack needs one scoring harness applied to both sides.
What changes when you swap the model behind a PolyAI-built agent?
Raven is the default model, with GPT-5, Claude, and Gemini available as swaps. A swap changes wording, refusal behavior, tool-argument formatting, and pacing while the flow definition stays byte-identical, so structural tests pass and behavior still moves. Re-run the persona, function-calling, and red-team passes after any swap rather than treating the change as configuration.
Does function-calling accuracy need testing separately from conversation quality?
Yes. They fail independently. A PolyAI-built agent can hold a natural, correctly paced conversation and still call the wrong tool, pass a malformed argument, or narrate a correct tool result back to the caller wrongly. Cekura scores the tool call and the narration as separate assertions, using Mock Tools that return controlled outputs so no live order, refund, or account system is touched during a test.
How do you test the phone line a PolyAI-built agent runs on?
Place real calls over a narrowband PSTN leg rather than wideband browser audio, inject DTMF sequences at the points a caller would press keys, and measure the full round trip against the ITU-T G.114 budget. Then ramp concurrent calls in steps until latency and error rates climb, which is where carrier limits and provider rate limits show up.
Does red-teaming a PolyAI-built agent need multiple turns?
Yes. Attack success climbs with turn count, and Crescendo, the multi-turn jailbreak described by Russinovich, Salem and Eldan, escalates a benign opening question by referencing the model's own replies until the model complies. A single adversarial prompt tests a filter. A long adversarial call tests the agent, which is the shape a real caller can produce on a phone line.
