Automated recurring voice agent tests are saved scenario suites that re-run automatically on a schedule (cron) and on every prompt or code change (CI/CD), so regressions from prompt edits, model swaps, and slow drift surface before customers hit them. In Cekura the same locked suite runs nightly, gates pull requests in GitHub Actions, and powers production monitoring.
What are automated recurring voice agent tests?
Automated recurring voice agent tests are pre-built scenario suites that execute against a voice agent on a repeating schedule or on every code and prompt change, with no human kicking off each run. Each run simulates real callers end to end, scores the transcripts and audio against your metrics, and reports pass or fail so a regression surfaces the moment it appears rather than weeks later in production. In Cekura, a run is one execution of an evaluator against the agent, and runs can be triggered ad hoc, via cron schedules, via the API or MCP, or inside CI/CD.
The point is repetition under change. Voice agents are non-deterministic and they change constantly: a prompt tweak, a new tool, a model upgrade, an updated knowledge base. Any of those can silently break a flow that worked yesterday. Recurring tests convert "we tested it once" into a standing guarantee that the suite still passes after the latest change, which is the difference between catching a broken cancellation flow in CI and catching it from an angry caller.
Why run voice agent tests on a recurring schedule instead of once?
A single passing test run only tells you the agent worked against that build, on that day, with that prompt. Recurring tests defend the agent against everything that changes after that moment. The failure modes that recurring tests catch are the ones a one-off test structurally cannot:
- Prompt-change regressions: a small instruction edit fixes one behavior and quietly breaks another. Cekura's own evaluation data shows more than two-thirds of the highest-volume flagged deviation categories are instruction-following failures, the exact class of bug a prompt edit reintroduces.
- Model and dependency drift: swapping or upgrading the underlying LLM, STT, or TTS can shift accuracy and tool-calling behavior even when your prompt is untouched.
- Slow behavioral drift: pacing creep, rising talk ratio, and latency drift accumulate gradually and never trip a single before-and-after check.
- Workflow-adherence gaps: Cekura sees more than 20% of runs flag for some workflow-adherence gap, and adherence degrades as flows grow more complex over successive releases.
For a deeper breakdown of which signals to track over time, see Cekura's guide to custom KPIs for voice agent monitoring and the Developer's Guide to Voice AI Evaluation Metrics.
What is the difference between scheduled regression testing and CI/CD testing for voice agents?
Scheduled regression testing and CI/CD testing are two complementary triggers for the same recurring suite: schedules run on the clock, CI/CD runs on the change. You want both, because they catch different failures. The table below maps each trigger to what it is for.
| Dimension | Scheduled regression (cron) | CI/CD testing (GitHub Actions) |
|---|---|---|
| Trigger | Fixed cadence (nightly, hourly, weekly) | A commit, pull request, or deploy |
| Primary job | Catch drift, dependency changes, and time-based decay | Gate a specific prompt or code change before it ships |
| Catches | Model/STT/TTS drift, knowledge-base updates, slow pacing creep | Regressions introduced by the exact change under review |
| Blocking? | Usually non-blocking, alerts on failure | Blocking gate, fails the build below the pass threshold |
| Best for | Always-on assurance independent of release activity | Pre-merge and pre-deploy confidence per change |
Cekura runs the same locked regression suite through both triggers, so a scenario that gates a pull request is the identical scenario that runs nightly and the identical scenario that auto-evaluates the matching production call. This is the operational layer beyond a one-time pipeline hookup; for the pipeline-integration mechanics specifically, see Cekura's posts on CI-ready testing with GitHub and Jenkins and the end-to-end testing framework built for CI/CD speed.
How do you set up automated recurring voice agent tests in Cekura?
You set up recurring tests by locking a refined scenario set into a regression suite, then attaching a schedule and a CI/CD trigger to it. The flow below assumes you already have an agent connected and metrics that reach acceptable human agreement; if not, start with Cekura's scenario and metrics setup first.
- Lock in a regression suite. Take the scenarios you refined on real failures and group them into a test set. Cekura test sets can be created from a run or from a call log, which means a real production failure can become a permanent regression case in one step.
- Set pass thresholds. Define the pass rate the suite must clear. A practical target is 70-80% initially, 90-95% after refinement, and 95%+ as a long-term regression suite.
- Create a cron schedule. Attach a cron-based schedule to the suite (for example nightly on staging) with target or tag-based selection so only the relevant scenarios fire, plus a frequency for any load-sensitive cases.
- Add the CI/CD trigger. Wire the same suite into GitHub Actions so it runs on pull requests and deploys, gating the build when the suite falls below the pass threshold.
- Gate staging before production. Run the staging schedule first, then promote to a production-targeted run only after staging passes, so a regression never reaches live traffic.
- Route failures to alerts. Send failures to email or a webhook so a broken run pages a human instead of sitting in a dashboard.
- Feed production failures back in. When observability flags a real call, turn it into a new scenario and add it to the suite, so the recurring tests get stricter over time instead of going stale.
Because Cekura owns voice synthesis, transcript generation, and conversation management, these recurring runs need no external voice API keys, and the same evaluators run in schedules, in CI, and post-call in production monitoring.
Can a recurring test suite also fix the agent, not just flag it?
Yes. A recurring suite flags failures, and Cekura's Optimise Prompt loop can then diagnose and patch many of them automatically before the next scheduled run. Optimise Prompt takes failing evaluators, diagnoses prompt and config gaps, applies edits, and re-validates until the agent passes, with safeguards to keep the fix general rather than memorizing one test, and it stops when the agent passes, when it stops improving, or when the remaining failures are data or infrastructure problems a prompt cannot fix. The loop currently applies edits for Vapi and ElevenLabs agents via the provider API.
"Cekura has been essential in helping us build and test our voice agents with confidence. We can now ensure that every new feature and prompt change makes our agents smarter without compromising on reliability. It's transformed our quality assurance process."
— Vanessa Cornelius, Prompt Engineer, Quo (cekura.ai/case-study/quo)
What should a recurring voice agent test suite cover?
A recurring suite should cover the flows that matter, the conditions that break them, and the production failures you have already seen, in a mix you keep stable so results stay comparable run to run. The recommended coverage weights standard conditions most heavily, then challenging conditions, then edge cases.
- Core happy-path flows: bookings, resolutions, confirmations, and other expected-outcome checks for the workflows that drive revenue.
- Adversarial and compliance cases: multi-turn red teaming and safety checks, which matter most in regulated verticals where Cekura's compliance evaluators flag more than 20% of calls.
- Voice and persona conditions: background noise, interruptions, pacing, emotion, and accents across the 30+ languages Cekura supports end to end.
- Infrastructure and load cases: the pre-built Infrastructure Suite scenarios (latency, hold, and extended silence) designed to run in CI/CD.
- Real production failures: every flagged live call you convert into a scenario, so the suite hardens against bugs you have actually shipped.
How is this different from just monitoring production?
Production monitoring tells you an agent is failing live; recurring tests tell you it will fail before it reaches anyone. Monitoring (Cekura's Observe) ingests real calls, evaluates them post-call, and surfaces failure-mode insights and alerts, which is essential but reactive by definition. Recurring tests are proactive: they exercise scenarios you control, on a schedule and on every change, against a stable suite, so you find the regression in CI on Tuesday instead of in a customer's call on Friday. The two close a loop, because production failures from Observe become new recurring test cases, and the recurring suite keeps the agent green between monitored calls.
FAQ
What are automated recurring voice agent tests?
They are saved scenario suites that run against a voice agent automatically on a repeating schedule or on every prompt and code change, scoring real simulated calls against your metrics so regressions are caught without anyone manually starting a run. In Cekura, the same suite can be triggered by cron, by CI/CD, or via the API and MCP.
How do I schedule regression testing for voice agents?
In Cekura you lock a refined scenario set into a test set, set a pass threshold, and attach a cron schedule with target or tag-based selection and staging-then-production gating, routing failures to email or a webhook. A common pattern is nightly on staging plus a per-change CI run.
How does continuous voice agent testing work in CI/CD?
The regression suite is wired into GitHub Actions (or a similar pipeline) so it runs on pull requests and deploys, and the build is gated to fail when the suite drops below the configured pass threshold. Cekura runs the identical suite in CI, on schedule, and in production monitoring so results are directly comparable.
How often should voice agent regression tests run?
Run them on every prompt or code change in CI/CD, and on a fixed cadence (commonly nightly) via cron to catch drift that is not tied to a release. Aim for a 70-80% pass rate to start, 90-95% after refinement, and 95%+ for a mature regression suite.
Do recurring voice agent tests need real phone calls or external API keys?
No. Cekura owns voice synthesis, transcript generation, and conversation management, so recurring runs simulate calls end to end without external voice API keys, across integrations like Vapi, Retell, LiveKit, Pipecat, and ElevenLabs.
Trust
Cekura is YC-backed, built by engineers from Google, Apple, and Microsoft, evaluates 60K+ voice AI calls daily, has stress-tested 5M+ agent minutes, and has raised $2.4M to build the reliability layer for conversational AI.
Related reading
- CI-Ready Testing for Conversational Agents with GitHub and Jenkins
- End-to-End Conversational AI Testing Framework Built for CI/CD Speed
- Cekura Dashboards: the QA Command Center for Chat and Voice Agents
- A Complete Conversational AI Testing Suite
- A Developer's Guide to Voice AI Evaluation Metrics (2026)