One successful test call does not show whether a voice agent can handle the busiest hour of the day. Under load, speech services, model limits, booking systems and telephony can fail at different points.
AI voice agent load testing should exercise whole conversations at realistic concurrency. The useful result is the supported call volume at acceptable quality, together with a clear plan for overflow and failure. These seven tests help establish that operating range.
TL;DR
- Test active conversations with audio and tool calls, not only connected silent calls.
- Distinguish call start rate from the number of calls already in progress.
- Measure caller-side delay, completed tasks, disconnects and duplicate actions.
- Test bursts, sustained traffic, provider limits and recovery separately.
- Set release thresholds from the business use case and measured baseline; there is no universal safe concurrency figure.
Build a useful test record
| Measurement | What it tells you |
|---|---|
| Call start rate | How quickly new calls are attempted |
| Active call concurrency | How many conversations are in progress |
| End of speech to audible reply | Delay experienced by the caller |
| Task completion | Whether the business objective succeeds |
| Tool errors and retries | Whether connected systems sustain the load |
| Unexpected disconnects | Whether callers lose the session |
| Duplicate actions | Whether retries create repeated business changes |
Twilio's guidance distinguishes calls per second and concurrency. A system may accept calls quickly but struggle to sustain long conversations, or handle active calls while queuing new outbound starts.
1 Establish the single-call baseline
Run representative calls through the intended telephony and audio path. Include short enquiries, long explanations, interruptions and calls that use backend tools. Record warm and cold behavior separately.
Use the same scenario set for later stages so changes have a meaningful baseline. Capture call setup time, caller-side response delay, completion and tool results.
What it reveals: Existing quality problems that higher load would amplify. Limitation: A single call cannot establish capacity or resistance to shared bottlenecks.
2 Ramp up concurrent conversations
Increase the number of active calls in controlled stages while keeping a realistic mix of durations and tasks. Hold each stage long enough to observe stable behavior and failures.
For an illustrative pilot, a team might examine 5, 10 and 20 active calls before choosing further stages. Those values are examples, not recommendations for every platform. Stop or reduce load when the agreed failure conditions occur.
What it reveals: The point where quality or completion begins to degrade. Limitation: Smooth ramps can conceal problems caused by sudden bursts.
3 Simulate a call-arrival burst
Create a concentrated increase in call attempts similar to a campaign response, outage or opening-hour rush. Measure how dispatch, queues and overflow routing respond. Check whether callers hear a useful message and whether the system avoids leaving sessions stranded.
LiveKit's load testing tools include ways to exercise media and agent dispatch behavior. Infrastructure tests are useful, but the release decision also needs realistic conversation and business task tests.
What it reveals: Admission, dispatch and overflow behavior. Limitation: A brief burst may not expose memory growth or slow dependency exhaustion.
4 Run a sustained soak test
Maintain a representative workload long enough to observe the relevant operating cycle. Include long calls, repeated tool use and session turnover. Monitor memory, resource usage, queue age and whether completed sessions release their resources.
Choose the duration from the suspected failure mechanism and production pattern rather than a fixed industry rule. Record any gradual increase in response delay or error rate.
What it reveals: Resource leaks and degradation over time. Limitation: A soak test is only as representative as its traffic distribution.
5 Exercise backend and model limits
Load the booking, CRM or support workflow through test environments and approved quotas. Introduce controlled latency, rate-limit responses and timeouts. Confirm that retries are bounded and that the agent does not claim a write succeeded before the backend confirms it.
Use correlation identifiers and idempotency protections for state-changing tools. The voice agent CRM integration guide explains related mapping and failure handling considerations.
What it reveals: Whether one dependency limits useful capacity. Limitation: A mock that always returns immediately will underestimate real failure risk.
6 Test handoffs and overflow under load
Simulate a human transfer when the target is busy, unavailable or does not accept. Check queue behavior, caller messaging, context transfer and the approved fallback.
Do not treat a connected transfer leg as proof that a qualified person accepted responsibility. Include the business outcome in the record. Use the interruption and handoff testing guide for the detailed single-call checks, then repeat the relevant cases under load.
What it reveals: Whether escalation remains usable at peak demand. Limitation: Staffing and contact-center configuration affect the result.
7 Verify degradation and recovery
Remove or slow a dependency in a controlled environment, restore it, then observe recovery. Confirm that the system releases abandoned sessions, drains queues safely and avoids replaying completed actions.
Repeat a normal baseline after recovery to detect hidden state or resource problems. AssemblyAI's load testing guidance emphasizes realistic concurrent traffic; apply the same realism to the recovery phase.
What it reveals: Whether the service returns to an acceptable operating state. Limitation: A single fault exercise does not cover every dependency combination.
A realistic scenario mix
For a fictional appointment service, combine availability questions, booking attempts, rescheduling, cancellations and human escalation. Have simulated callers speak, pause and interrupt. Include unavailable appointment slots and a booking timeout followed by a retry.
LiveKit's agent simulations distinguish audio and text testing paths. Audio-based tests are needed when the behavior being assessed includes recognition, speech synthesis and perceived timing. Text tests remain useful for faster checks of reasoning and tool selection.
Pros and cons of automated load tests
Automation makes stages repeatable and helps expose regressions. Its limitations include scenario bias, simulated speech that is easier than real callers, and telephony or provider costs. Pair automation with human listening reviews and a controlled production rollout.
Set release criteria before the run
- Define the caller experience and business completion requirements by scenario.
- Record sample counts and latency percentiles rather than averages alone.
- Specify stop conditions for disconnects, duplicate actions and unsafe responses.
- Confirm which system owns overflow and who can initiate rollback.
- Retain scenario, agent and provider versions with the result.
Use the voice agent KPI guide to align the release report with ongoing monitoring. Capacity is the highest tested workload meeting the agreed conditions, not the largest number of calls that connected once.
Frequently asked questions
Can a normal HTTP load test replace voice testing
No. It may test a backend endpoint, but it does not reproduce audio streaming, turn-taking, recognition or caller-side playback.
Are silent connected calls useful
They can test a narrow connection limit. They do not establish the capacity of a complete speech and tool workflow.
What is the right concurrency target
Use expected peak traffic, duration and an agreed margin, then test it. Provider plans and your deployment determine the available capacity.
Should a failed test always block launch
Define the policy in advance. Material task failures, privacy issues or duplicate state-changing actions need resolution before exposing the affected workflow to customers.
Launch with a measured operating range
Keep capacity evidence with the deployment plan and retest after meaningful configuration changes. Explore LuMay Voice Agents to discuss call workflows, integrations and deployment requirements.
Sources and further reading
- Twilio Help Center
- Load testing | LiveKit Documentation
- Agent Simulations | LiveKit Documentation
- How to load test a voice agent before launch (plus a production-readiness checklist) | AssemblyAI
Sources and linked destinations checked on 1 November 2026. Product capabilities and documentation can change.
