Recommended infrastructure and deployment flow optimized for reliability, scale, and operational clarity.
Provision dedicated node pools for each exam region don’t run AI agent and caching workloads on the same hardware if you want predictable latency. We had a cache eviction storm hit AI inference pods once; bad placement, recovery took an hour.
Configure L4/L7 load balancer rules so that proctoring streams, quizzes, and bot inference routes hit isolated backend pools. If not split, you’ll see cross-traffic spikes that look like internal DDoS after 10k+ users connect within seconds.
Deploy AI agents as stateless containers, but never store event logs and student state with agent code when an agent goes down, session restoration is impossible if all data was ephemeral.
Set up Redis and Postgres with region-aware sharding single shared DB instances will throttle and stall under high churn (we’ve seen >100rps/region swamp generic DBaaS). Adjust retention and snapshotting policies to avoid hour-long rollbacks after accidental data wipe.
Connect origin backend to S3-compatible storage for persistent logs and media watch for backpressure on storage during event replay; we witnessed S3 throttling during a 20TB log dump one semester.
Integrate Prometheus+Grafana for resource, queue, and per-agent latency metrics; don’t just monitor HTTP codes. Missed queue lag in one rollout led to slow grading for 2,000 students.
Test rollback drills at real load at least a week ahead of major exams; most teams have never tried rolling back a regional backend at >10,000 concurrent sessions, so the first real-world rollback is always a mess if you don’t rehearse.
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