Workflow Design
Agentic workflows start with input design
Most agent failures begin before the model responds: with vague, inconsistent inputs.
By JirakJ
5 min read
The moment to pay attention is not when somebody says "we should use AI." It is when different users feed the agent different context and get unpredictable output.
I would rather see one honest workflow map than ten polished AI use-case slides. From there, the work is to find the narrowest responsible improvement, not the loudest demo.
A small field test
Take one recent example of this workflow and replay it from request to finished output. The weak point will usually match the complaint: different users feed the agent different context and get unpredictable output.
Where the human stays
The human work is deciding what good means, what risk is acceptable and when a draft is not good enough. That judgment should be designed into the flow, not left to chance.
What to change first
Create input templates, required fields and examples before automation. Do that before choosing a platform or adding another automation layer.
What I would keep
Keep the input schema and sample set. It becomes the reference point when the team forgets why the workflow was changed in the first place.
Monday morning checklist
- • Turn the next meeting into a decision log instead of another broad AI discussion.
- • Write down the artifact that would make the work reviewable: in this case, a input schema and sample set.
- • Decide who owns the next version if the first version works.
- • Mark the part of the workflow where human judgment must stay visible.
If this sounds familiar
Start with one workflow. FlowMason AI can map it, identify the right intervention, and define whether the next step should be a prototype, agent, documentation pipeline or delivery system.
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