workflows
7 posts tagged here.
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A process map becomes useful for AI when it becomes an authority map
Process maps are useful before agent roadmaps, but they become much more valuable when they show where authority changes hands: what an agent may observe, prepare, recommend, execute, escalate, or never touch.
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Process maps should come before agent roadmaps
As enterprise AI shifts from pilots to production, the practical bottleneck is not choosing the next agent to build. It is understanding the workflow well enough to decide where agency, automation, review, and rollback actually belong.
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The real product of an AI automation agency is workflow judgment
As agent tooling gets easier, the scarce part of AI automation is no longer assembling the agent. It is knowing which workflow deserves automation, where authority should sit, and what evidence proves the system is working.
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Most teams put human review too late in AI workflows
As AI products gain longer-running execution surfaces across browsing, coding, and design, the practical mistake is not having too little review in the abstract. It is placing review too late, after the system has already done expensive, risky, or hard-to-unwind work.
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How to tell whether an AI workflow should be a workflow, an agent, or a hybrid
A practical way to choose between deterministic automation, agentic execution, and a mixed design — based on risk, ambiguity, exception load, and how much judgment the work actually needs.
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The real battle in AI is not just model quality. It is who owns the work loop.
The important shift in AI is not only better models. It is that labs and platforms increasingly want to own the connectors, interfaces, and managed loops where real work happens.
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The most useful AI workflows look more like delegation than prompting
The highest-value AI workflows usually come from better task design, supervision, and review — not from treating prompting as the entire job.