AI architecture5 min read
When the workflow does not need an agent.
Agents are useful when a system needs to choose among tools, adapt a plan as new
information appears, or carry work across multiple steps. They are not automatically
better than a deterministic workflow.
If the task can be expressed as a stable sequence—retrieve the data, apply a known
rule, produce an output—an ordinary workflow is often easier to evaluate, secure,
operate, and explain.
Start with the least autonomous design that can solve the problem reliably.
Add agentic behavior when the workflow genuinely benefits from judgment at run time,
not because “agent” is the fashionable architecture.
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Data foundations6 min read
The data layer AI actually depends on.
An AI system can only reason from the context it is given. If two dashboards disagree
on revenue, ownership is unclear, or access policy lives only in someone's head,
putting a model on top does not make those problems disappear.
The useful foundation is usually less glamorous: authoritative sources, business
definitions, ownership, metadata, access rules, and enough quality signals to know
when an answer should not be trusted.
AI readiness is not a separate platform. It is often the discipline of making existing data understandable and governable.
The goal is not perfect documentation. It is sufficient structure for people and
systems to make the same important decisions consistently.
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AI delivery5 min read
How to tell if an AI workflow is worth building.
The compelling demo is rarely the difficult part. The real question is whether the
workflow saves enough effort, improves enough quality, or enables something valuable
enough to justify operating it.
Before building, establish the current baseline: time spent, error rate, handoffs,
volume, and what happens when the workflow fails. Then compare the AI-assisted version
against that baseline—including review effort and operating cost.
A workflow that saves five minutes but creates ten minutes of checking has not been automated.
Scale only when the evidence supports it. Otherwise simplify the design, narrow the
scope, or leave the existing process alone.
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