Reliability Testing
Learn to ensure AI agents perform consistently and handle failures gracefully
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Stress testing reveals how your agent behaves under high load, concurrent requests, and resource constraints. Production agents face spikes in traffic, API rate limits, and competing workloads. Testing under stress identifies bottlenecks, failure modes, and performance degradation before users encounter them.
Types of Stress Tests
Load Testing
Gradually increase request volume to find the breaking point.
- • Start: 10 requests/sec → End: 100 requests/sec
- • Monitor: Success rate, latency, error types
- • Goal: Identify maximum sustainable load
Spike Testing
Sudden burst of traffic to test elasticity and recovery.
- • Normal load → 10x spike → Return to normal
- • Monitor: Recovery time, request queuing
- • Goal: Ensure graceful handling of spikes
Endurance Testing
Sustained load over extended period to find memory leaks.
- • Duration: 24-72 hours at 70% max capacity
- • Monitor: Memory usage, degradation trends
- • Goal: Detect resource leaks and drift
Chaos Testing
Randomly inject failures to test resilience and recovery.
- • Kill processes, network partitions, API failures
- • Monitor: Failover behavior, data consistency
- • Goal: Validate fault tolerance mechanisms
Interactive: Stress Test Simulator
Configure load parameters and see how your agent would perform:
When load exceeds capacity, agents should degrade gracefully: queue requests, return cached responses, or provide partial results. Failing fast is better than hanging indefinitely. Set clear timeouts, implement circuit breakers, and communicate status to users ("High load - 30s wait time").