What's the difference between an "AI agent" and regular automation?

Regular automation follows a fixed set of rules — if this happens, do that. An agent uses a model to decide what to do next based on context, so it can handle cases the rules didn't anticipate. That flexibility is also the tradeoff: agents are harder to test and more likely to do something unexpected than a fixed workflow.

Do I need a separate agent for every task, or one that handles many?

Both patterns are represented here. Narrow agents built for one job (drafting replies, triaging tickets) tend to be more reliable and cheaper to run; general-purpose agent platforms cost more but avoid stitching together multiple tools. Start narrow if you're new to this category — it's easier to tell whether it actually saved time.

How do I evaluate reliability before rolling one out to the whole team?

Run it in shadow mode first — let it draft or suggest without acting — and compare its output against what a person would have done for a few weeks. Vendor demos are optimized for their best cases; your data and edge cases are what actually determine reliability.

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Frequently asked questions

What's the difference between an "AI agent" and regular automation?

Do I need a separate agent for every task, or one that handles many?

How do I evaluate reliability before rolling one out to the whole team?