AI Orchestration Systems — Module 7 at production scale

One workflow is a script; a company's AI runs hundreds of workflows, thousands of executions a day, for weeks-long lifecycles. Orchestration is the layer that makes that reliable, and it's Module 7's concepts plus classic distributed-systems hygiene:

Durable execution — the keystone concept: long-running workflows (a document pipeline with a human-approval step might live for days) must survive process crashes, deploys, and restarts, resuming from their last completed step. Recognize it: Topic 26's checkpointing, generalized from training state to workflow state — every step's inputs/outputs persisted, the workflow replayable to its frontier. Temporal/Inngest-class engines productize this; a job table in Postgres plus idempotent steps is the artisanal version; the property, not the vendor, is the requirement.

Around that keystone: queues and schedulers (event-driven beats cron; backpressure when the model gateway rate-limits), per-step hardening (retries with backoff, timeouts, circuit breakers — an LLM step is a flaky network dependency and gets treated like one), the model gateway underneath everything (Topic 70's shell as shared infrastructure — one place for keys, routing, fallbacks, and fleet-wide rate-limit governance, so forty workflows don't independently DDoS your provider quota), and tracing (Topic 60's lesson industrialized: every execution a browsable trace of steps, prompts, tool calls, costs — the difference between debugging and archaeology).

Two orchestration-specific insights worth the price of the topic: humans are a service. At scale, human-in-the-loop (Topics 57/80) means review queues as first-class workflow steps — with assignment, SLAs, and escalation — and once modeled that way, humans slot into your architecture as another dependency with latency (hours) and error rates (Topic 74's 70% agreement), plannable like any other. And version everything together: a workflow's behavior = code × prompts × model versions × eval baselines; deploy them as one versioned unit (Topic 75's loop at fleet scale), or live the special misery of "the prompt changed but the eval baseline didn't."

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