Start with a workflow map
Write down what starts the workflow, what data is allowed in, what the model should produce, and what action follows. If the action is consequential, add an approval step.
Make outputs predictable
Use a schema, examples, required fields, and validation. Route incomplete or uncertain outputs to a review queue rather than silently continuing.
Protect credentials and data
Store credentials using the platform’s supported mechanism, restrict access, and minimise the data passed to external services. Add logs that help you troubleshoot without exposing sensitive content.
Measure the automation
Track success rate, failure categories, latency, cost, manual overrides, and user-reported issues. Compare this with the AI automation project guide and agent fundamentals.
Build reliable AI workflows
Review the ISS AI & Agentic Systems curriculum.
View AI curriculum →Editorial scope
Nodes, integrations, plans, and policies change. Verify current behaviour in the official documentation.