AI Workflow Engineer: Design, Build, and Operate

Generating one acceptable response from a large language model is relatively straightforward. Building a system that behaves reliably across repeated runs, including under malformed, ambiguous, or adversarial inputs, requires a different set of engineering practices. AI workflow engineering focuses on turning LLM calls into reliable, testable automation. This course focuses on those practices. I built this course based on my work in adaptive AI and intelligent computing and from seeing the same pattern across GenAI systems: teams get a workflow working once, then discover it lacks validation gates, retry logic, and explicit failure handling. A production workflow needs to handle failure safely, not just succeed under ideal conditions. That means designing controls such as structured outputs, validation, retries, tool handling, retrieval, and human oversight into the workflow from the start rather than adding them reactively after a production failure. This course teaches that discipline through one running example designed around production constraints: a support inbox workflow that triages incoming messages. You'll write prompt specifications and JSON contracts, call the Gemini API directly over HTTP, and build the workflow in n8n with branching, validation, retries with backoff, and execution logging, then apply the same workflow pattern in Zapier and Make. You'll extend it into a bounded agent with tool allowlists, step limits, and human approval gates, ground its responses in approved policy documents with retrieval-augmented generation, and evaluate and operate it using test sets, evaluation rubrics, instrumentation, and safeguards against privacy leaks and prompt injection. By the end, you'll be able to design, build, and operate AI workflows that behave predictably under defined conditions and hand one to a colleague with clear failure handling and safeguards in place.