Workflows & Automation
AI workflows chain multiple steps — models, tools, and data sources — into processes that run reliably without constant human intervention. This section covers the design patterns for reliable AI automation, from choosing between deterministic and agentic approaches to building in observability and appropriate human checkpoints.
In This Section
Deterministic vs Agentic
When to hard-code a workflow vs let the model decide — the reliability and control tradeoffs of each approach.
Orchestration Tools
n8n, LangGraph, and other orchestration frameworks — what each is suited to and how to choose between them.
Human-in-the-Loop
Where to insert human review and approval gates — and how to design them so they actually catch errors without becoming bottlenecks.
Error Handling & Observability
How to make AI workflow failures visible and recoverable — logging, retries, dead-letter queues, and tracing across steps.