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AI / OpenClaw Interview Questions

What is the difference between OpenClaw serializing messages per session and running agents in parallel across sessions?

These two behaviors operate at different scopes and aren't in tension with each other.

  • Per-session serialization: messages within the same conversation are processed one at a time, in order, keeping that specific session's state consistent
  • Cross-session parallelism: separate, unrelated sessions can still run concurrently, since they don't share state that could conflict

Understanding this distinction matters for benchmarking and capacity planning, throughput scales with how many independent sessions run in parallel, not by processing individual messages within one session any faster.

What does per-session serialization keep consistent?
What does throughput actually scale with in this model?

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