Agent Operations
Every serious AI agent needs a failure path
The difference between a demo agent and an operated workflow is what happens when it fails.
By JirakJ
6 min read
The moment to pay attention is not when somebody says "we should use AI." It is when the agent works in happy-path demos but has no plan for bad inputs or uncertain outputs.
I would start with a blank page, not a tool comparison. From there, the work is to find the narrowest responsible improvement, not the loudest demo.
The mistake I would avoid
I would not begin by asking for a bigger AI plan. I would begin by asking why the agent works in happy-path demos but has no plan for bad inputs or uncertain outputs. Until that is understood, every tool choice is premature.
The useful version of the problem
Failure paths make agents safer to operate and easier to improve. That is a much cleaner target than becoming AI-enabled in some abstract way.
What I would put on the table
I would put a failure-path table and escalation rules on the table and make the team react to it. If people cannot agree on that artifact, they will not agree after the build either.
The small move
Define reject, escalate, retry and human-review states before launch. It sounds modest, but it creates a surface area for disagreement before money is spent.
Why it matters
A useful AI workflow should feel a little boring by the time it ships. Boring is often another word for operable.
Monday morning checklist
- • Open a shared document and describe the current workflow as it happens today, including the ugly parts.
- • Write down the artifact that would make the work reviewable: in this case, a failure-path table and escalation rules.
- • 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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