workflow-design
10 posts tagged here.
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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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An agent exception log should change the workflow, not just judge the agent
The useful exception log is not a scorecard for the agent. It is the repair list for the workflow that produced the exception.
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Escalation is not an error path for AI agents
As agent systems move from demos into real workflows, escalation should stop being treated as a failure fallback. It is one of the main design surfaces for making AI work usable, governable, and trusted.
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Agent frameworks are becoming control decisions, not library decisions
As Google, AWS, and the broader AI market push agent-building tools into the enterprise, the important choice is no longer only which framework feels easiest. It is which control model a team is committing to.
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The next AI product shift is from assistants to workbenches
Recent launches from OpenAI, Anthropic, and infrastructure partners point to a practical shift: the market is moving beyond generic AI assistants toward role-shaped workbenches designed around real jobs, artifacts, and handoffs.
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The most underrated layer in AI systems is preparation
A lot of teams jump too quickly from AI recommendations to AI execution. The more practical path is often a stronger preparation layer that stages work, narrows risk, and earns trust before full autonomy.
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The next AI bottleneck is operational discipline, not model intelligence
This week’s product signals point in the same direction: the hard part is no longer only smarter models. It is budget control, permissions, runtime design, and the operating discipline required to let capable systems do real 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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If your agent keeps sending reminders, you built a reminder, not a worker
A simple design test for autonomous AI workflows: if the system keeps nudging a human instead of progressing the task, the job was designed as a reminder loop, not an execution workflow.