AI Workflow Automation

6 Interactive Sessions

Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.

1. 1

   The Trigger — you can't process an event you dropped

   Before a workflow can classify, draft or route anything, the thing that started it — the event — has to be received, validated, and durably recorded. Events, not direct calls.

2. 2

   The AI Steps — four nodes, one interface

   The ticket needs four AI operations — classify, summarize, extract, draft. Make each a reusable NODE with the same input→AI→typed-output interface, and any workflow can compose them.

3. 3

   The DAG — a workflow is a graph, not a script

   The steps aren't a straight line. Classify first, then branch: billing tickets get extracted and routed to billing, technical tickets get summarized and routed to support. That branching is a DAG, and the engine walks it.

4. 4

   When Things Fail — a dropped ticket is a lost customer

   The generator times out on #8842. Without reliability, that ticket silently vanishes. With it: retry with backoff, then a dead-letter queue that parks the failure for a human, plus idempotency so a retry never sends two replies.

5. 5

   See It Run — an automation you can't see is a liability

   Build the dashboard that makes the automation observable: a visual workflow builder, a live event feed, a per-event execution timeline (which nodes ran, timings, status), and a DLQ view — so you can always answer 'is it running, and where did #8842 get stuck?'

6. 6

   Run It at Scale — production is rate limits, cost, alerts, audit

   Monday morning, 5,000 tickets hit at once. Without rate limiting you blow the AI budget and hit provider limits by 9:05am. Production means rate limits, per-workflow cost caps, alerting when the DLQ grows, and an audit trail of every automated action.

What you walk away with