™AI
AI agents vs traditional automation
Traditional automation follows a path you defined. An agent decides the path. That single difference drives every trade-off between them.
By Tomryn ·
Rule-based automation is deterministic: given the same input, it does the same thing, every time, and you can read the rules to know what will happen. An agent is given a goal and some tools, and works out its own sequence of steps.
The appeal of the second is obvious. The cost is that you can no longer predict the exact path, which matters a great deal in some contexts and very little in others.
Where rules win
- Anything involving money moving, or a legal or regulatory consequence.
- High-volume, low-variation work where predictability is the whole point.
- Processes that must be auditable step by step.
- Situations where a wrong action cannot be undone.
Where an agent earns its place
- Input that arrives in inconsistent formats and has to be interpreted before anything else can happen.
- Work where the right next step genuinely depends on what was found in the previous one.
- Tasks with a long tail of exceptions that would each need their own rule.
- Research and gathering tasks, where the output is reviewed by a person anyway.
The practical answer is usually both
The pattern we use most often is an agent at the messy edge and rules in the middle. The model reads the incoming email, extracts what it can and decides how to categorise it; deterministic code then does the actual work of creating the record, applying the pricing and sending the confirmation.
That keeps the unpredictable part confined to interpretation, where a mistake is visible and correctable, and keeps the consequential part fully deterministic.
Questions worth asking before choosing
- What happens if this takes the wrong action, can it be reversed?
- Does anyone need to be able to explain, later, exactly why it did what it did?
- How much genuine variation is there in the input, honestly measured?
- Is a person reviewing the output before it has any effect?