Custom Agents
Write output standards before agent prompts
If you cannot describe a good output, you cannot judge whether the agent works.
By JirakJ
4 min read
The expensive part is rarely the model. It is the missing agreement around the work. The prompt is detailed but the team has no shared definition of acceptable output. That is the real buying signal.
If nobody can explain the current flow in plain language, automation will only make confusion faster. For teams specifying AI agents, the practical question is whether the workflow is ready to be made more reliable.
What I would not buy
I would not buy another broad discovery deck for this. The useful starting point is smaller: the prompt is detailed but the team has no shared definition of acceptable output.
The first honest artifact
Produce a output standard document and let the team challenge it. The disagreement is valuable because it shows where the workflow is still vague.
The move
Write accepted examples, rejected examples and quality criteria first. If that cannot be done cleanly, a build will not magically make it clean.
The commercial reason
Output standards make prompting, testing and review much easier. That is what a buyer can feel: fewer loose ends, fewer mystery handoffs and less dependence on heroic follow-up.
Monday morning checklist
- • Pick one painful step and define the input, output, owner and review rule.
- • Write down the artifact that would make the work reviewable: in this case, a output standard document.
- • 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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