Proof before your peak
Journey Load &
Performance Testing
Agents do not degrade the way infrastructure degrades. Long before anything fails over, they get slower, then vaguer, then wrong. Find that point before every release, migration, and seasonal peak.
Latency at peak
Measured at every load step
Hallucination under load
Accuracy scored as volume climbs
Journey completion
End to end, not endpoints
SLA adherence
Held to your targets
Reuse scenarios
Set up once. Run it at scale.
Reuse the scenarios you built for functional testing and run them under high concurrency. No separate scripts, rebuilt test cases, or new load-testing workflow.
Full path
Test the AI agent and everything around it
Put the full path under load, from carrier and call setup through IVR, routing, and the bot. Or isolate each layer to see what breaks first.
Live diagnosis
See it live, then diagnose
Watch sessions and errors as the campaign runs. Then drill into results by test case so teams get evidence they can act on.
Spot degradation before it impacts customers
Find where quality starts slipping
Infrastructure fails loudly. Agents fail quietly, with every component still reporting green. Cyara ramps concurrency and request rate until quality moves, then names the number where it moved. That number is the one your capacity plan needs.
- Configurable concurrency, CAPS, and RPS
- Ramp-up, not step load
- Traffic distribution you control
- Quality measured every step
- The degradation point, named
Ensure quality at peak demand
Quality scored under load, not assumed
Monitoring says the agent stayed up. It can’t tell you it stayed right. Past a certain concurrency, the agent still answers, confidently and wrong. Score hallucination rate, journey completion, and SLA adherence at every load level.
- Hallucination rate under load
- AI-powered assertions at peak
- Confidence-based response validation
- Journey completion, not just response
- Objective-based, not replayed scripts
Test real customer traffic at scale
Real channels at real concurrency
Synthetic API pressure proves the model can take the requests. It doesn’t prove the journey survives. Cyara drives real carrier voice calls, real SMS with OTP steps, and real web and chat sessions at peak, on capacity booked in advance.
- Real carrier voice legs
- Inbound and outbound SMS with OTP
- Web and chat sessions, APS-controlled
- Voice and bot API channels
- Voice capacity booked in advance
Expose bottlenecks before they become failures
Surface rate limits before peak hour
The ceiling is often not yours: a model provider’s rate limit or a queue behind an API you don’t control. Cyara correlates load with response times, call states, and errors live, so limits surface in a test window.
- Live campaign metrics
- Load correlated to failures
- Model rate limits exposed
- Queue and routing bottlenecks
- Drill-down reports by test case
Why it’s different
What traditional load testing can’t see
Most load tests prove the system stays up. Agentic load proves it stays right, across every test type and peak event, from Black Friday to a cloud migration cutover.
| Capability | Traditional and API load testing | Cyara Agentic Load |
|---|---|---|
| What’s measured | Uptime, throughput, and errors | Quality, accuracy, and completion at every load step |
| Hallucination under load | Not measured | Scored with AI-powered assertions |
| Channels | Synthetic API requests | Real carrier voice, SMS with OTP, web, and chat |
| Test types | Usually load or spike only | Load, spike, stress, soak, scalability, and capacity |
| Test assets | Separate scripts to build and maintain | Reuses your functional test scenarios |
Proven at enterprise scale
Validate performance
before production
See how your agents and the systems behind them perform at peak.
40,000+
Concurrent AI agent interactions
Up to 90%
Less downtime
55+
AI agent vendors supported
Independent AI agent oversight
AI Agent Observability
Components that pass in isolation still have to survive the full journey. Validate the path as one continuous flow, voice to SMS and agent to human.