AI and workflow automation
AI workflow development
An AI workflow is a sequence of automated steps where one or more steps use an AI model. Building one that works reliably every day involves much more than writing a good prompt: the workflow needs a clear trigger, outputs in a predictable structure, rules that check those outputs, handling for errors and a record of what happened.
This page explains the building blocks of a dependable AI workflow and the development practices that keep it working as things change.
Illustrative diagram
- Trigger
- Prepare inputminimise data
- AI stepstructured output
- Validate
- Act or flag for review
- Log
The parts of an AI workflow
Trigger
What starts the workflow: a new email, a form submission, a file in a folder or a scheduled run.
Output: Input item
Preparation
Extract text, remove unneeded personal data, gather context from other systems.
Output: Clean input
AI step
The model reads, classifies, extracts or drafts, returning a structured result.
Output: Structured output
Validation
Rules check the output: required fields present, values in allowed ranges, confidence high enough.
Output: Accepted or flagged
Action and logging
Accepted results update systems; flagged ones go to a person; everything is logged.
Output: Completed item and record
Ask for structure, then check it
Free-form text from an AI model is hard for a workflow to act on. Reliable workflows ask the model for a defined structure, such as specific fields with specific allowed values, and then check that structure before using it. If a required field is missing or a value is outside what is allowed, the item is flagged rather than passed on.
Validation rules turn an AI step that is usually right into a workflow that knows when it might be wrong.
Planning for things that go wrong
| What can go wrong | Handling |
|---|---|
| AI provider temporarily unavailable | Retry with a delay; alert someone if it persists |
| Output missing required fields | Flag for human review |
| Input unreadable, such as a poor scan | Route to a person with the original attached |
| Connected system rejects the update | Log the error, keep the item, notify the owner |
| Unusual volume spike | Queue items rather than dropping them |
Development practices that keep workflows healthy
Testing
- A test set of real, anonymised examples
- Re-run the test set after every change
- Track results over time
Change control
- Prompts and configuration versioned
- Changes tested before going live
- A way to roll back
Visibility
- Logs of inputs, outputs and decisions
- Alerts for repeated failures
- A simple dashboard or report for the owner
Prompts are code
Version them
Small wording changes can change results; keep a history.
Test them
Run the test set before deploying a new prompt.
Document them
Explain why each instruction exists so others can maintain it.
When the AI model changes
AI providers update and retire models. A workflow tied to one model may behave differently after an update. Keeping a test set and running it whenever the model changes shows whether results are still acceptable before the change affects real work. It also makes switching providers practical if terms or prices change.
Giving the AI step the right context
AI steps work better when they receive the right context: a list of the categories to choose from, the customer's previous orders when classifying a support email, or the supplier list when reading an invoice. The preparation step gathers this from connected systems before calling the model. Too little context produces guesses; too much adds cost and may send data that is not needed.
Deciding what context each step needs is one of the most important design choices in an AI workflow.
Designing the review step
Items flagged for review should reach a person with everything they need: the original input, the AI's suggested result, the reason it was flagged and a quick way to accept, correct or reject it. Corrections should be recorded, because they show where the workflow struggles and provide new examples for the test set. A clumsy review step undermines the whole workflow, so it deserves as much design attention as the AI step.
Questions about AI workflow development
What is an AI workflow?
A sequence of automated steps where one or more steps use an AI model.
Why ask the AI for structured output?
So the workflow can check it with rules before acting on it.
What happens when the AI is unsure?
Validation flags the item and routes it to a person.
What is a test set?
Real, anonymised examples used to check the workflow after every change.
What if the AI provider changes its model?
Re-run the test set to confirm results before relying on the new model.
Can the workflow recover from errors?
Yes, with retries, queues and alerts designed in.
Can our team maintain it?
With documentation and versioned prompts, a technically confident team member can.
Where do reviewers see flagged items?
Wherever suits your team: a shared inbox, a task list or the system the result belongs in.
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