Throughput-based load balancing breaks down when streaming messages have heterogeneous processing costs — the fix is balancing on actual per-partition resource usage.
AI models do not fail due to bad coding; they fail due to an upstream change in the input. Combine contracts with circuit breakers to stop bad data from entering models.
Instrument a Python Flask service with OpenTelemetry auto trace requests, export metrics to Prometheus, and inject trace IDs into logs for observability in one setup.
Feature flags help teams move fast, but when they’re not cleaned up, they quietly add extra code, slow down performance, and make applications harder to maintain.
Cache reads with Redis, use @CachePut for write-through consistency, and prevent stampedes with distributed locks, then prove it works under load with JMeter.
Microservices assume predictable callers. AI agents break this with non-deterministic calls, fan-out, and retries. Here are 5 core assumption breaks and fixes.
Observability costs spiral when teams optimize for visibility, not cost. Fix it by making spend visible, sampling aggressively, and cutting low-value data.
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