How we turn a complex workflow into an AI agent system
The hard part of AI automation is rarely the model itself. The hard part is taking a business process with exceptions, incomplete data and several people involved, then turning it into a sequence that can be checked, improved and scaled.
Start with the workflow, not the chatbot
We begin by mapping the real path from the first event to the result. In a catalogue business, that can include an artist upload, file checks, category selection, tags, preview preparation, moderation, product publication and a customer order. Each handoff is written down with its input, output, owner, systems used and rule for success.
Split a difficult task into small decisions
A complex request should not be sent to one model with a long prompt and treated as finished. We divide it into steps: identify the task, collect source data, validate file and product information, classify the product, create a draft, check the result against rules, then route exceptions to a person. The output of one step becomes the reviewed input of the next.
Integrate specialised models where they add value
A language model can interpret a seller description and draft metadata. A vision model can check a preview against visual requirements. A rules layer can validate formats, dimensions and mandatory fields. For difficult decisions, the agent prepares evidence and options while a person approves the final action. Examples, role-specific instructions and evaluation sets improve each module over time.
Build an agent module, not a black box
This is how we built the agent module for our catalogue workflows. The orchestrator receives a new item, creates a task checklist, calls only the tools needed, records each result and returns the item either to the next step or to a human reviewer. The team sees what happened, why a product was flagged and what needs correction.
Measure the commercial result
After a pilot, we compare time from upload to publication, incomplete cards, moderation rework, discoverability, conversion and repeat purchases. The goal is a repeatable operating system that removes waiting, improves catalogue quality and gives the team more time for work that needs judgement.
Where the workflow is applied
The agent module supports a catalogue ecosystem with distinct destinations for different creators and products.