Scaling Workflows¶
A workflow that's correct at 1 run rarely stays correct unchanged at 100,000 runs a day. This subtopic is what changes — concurrency, cost, isolation — once a workflow becomes a fleet.
flowchart LR
J["Junior: measure per-run cost"] --> M["Middle: control concurrency and cost"]
M --> S["Senior: isolate failures at scale"]
S --> P["Professional: govern the fleet"]
Levels¶
| Level | Guide | You are done when |
|---|---|---|
| Junior | Measure what breaks past one run | You can measure per-run cost, latency, and identify what rate limits or quotas you'll hit first. |
| Middle | Control concurrency and cost | You can design worker pools, backpressure, and per-run budgets that hold at real volume. |
| Senior | Isolate failures at scale | You can design bulkheads, circuit breakers, and caching that prevent one bad dependency from taking down the fleet. |
| Professional | Govern the fleet | You can set SLOs, capacity plans, and cost attribution across every team's workflows. |
Practice rule¶
Before assuming a workflow "will just scale," run it at 10x, then 100x the volume you tested at, and write down the first thing that breaks. That's your actual scaling bottleneck — not a guess.
Related¶
- Reliability and Recovery — the retry and gate logic that has to behave correctly under concurrent load, not just in isolation.
- State, Memory, and Durability — checkpointing itself becomes a cost and throughput concern at fleet scale.
- Agent Evaluation — measuring whether the fleet is actually meeting its reliability and cost targets in production.