Treat AI Agents as Distributed Systems: The Completed Run Is the Real Metric
rachittshah · x · 2026-09-09
The author argues agents should be engineered as small distributed systems: the model is just one node in a request path full of queues, sync barriers, retries, caches, and external dependencies.
- Users experience the whole composition — admission, auth, state loading, planning, inference, retrieval, DB queries, code execution, eval, persistence, delivery — and that path adds more latency than any single inference setting
- Provider dashboards (TTFT, tokens/sec, I/O sizes) don't reflect this; the completed run is the useful unit of measurement
- Practical playbook: draw the dependency graph, instrument boundaries, assign deadlines, control fan-out, and keep enough execution state to explain how a request finished
- Faster models only help when inference sits on the critical path
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