AI Integration
Agents, Explained Without the Marketing
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The actual distinction
- Workflow: you define the steps; AI does the parts requiring comprehension
- Agent: the model chooses which tools to use and in what order, until it decides it is done
That difference matters enormously for reliability, cost and how you debug a failure.
Why workflows usually win in business
| Property | Workflow | Agent |
|---|---|---|
| Predictability | High | Variable |
| Cost per run | Known | Variable, sometimes surprising |
| Debugging | Straightforward | Considerably harder |
| Auditability | Clear | Requires effort |
| Handling novel situations | Poor | Better |
Businesses value the first four more than the fifth, almost always. That is why the fashionable answer and the right answer differ here.
Where agents genuinely help
- Research tasks where the next step depends on what was just found
- Diagnosis, where the questions to ask depend on the answers so far
- Long-tail cases too varied to enumerate as steps
Note that all three are exploratory rather than transactional. That is the pattern.
Constrain agents heavily
- A small, explicit set of tools rather than broad capability
- A step limit and a cost limit per run
- No write access to anything consequential
- A full trace of every step, retained
- A human checkpoint before anything irreversible
The hybrid pattern
A workflow for the known path, with an agent handling the specific step that is genuinely open-ended, inside strict limits.
That gets the predictability where it matters and the flexibility where it helps, which is what most real systems end up looking like.
Frequently asked questions
Are agents ready for production?
For constrained, low-stakes, well-monitored tasks, yes. For unattended work with real consequences, the reliability is not there yet for most businesses.
Why do agents cost more?
Each step is a model call and the number of steps varies. A task that usually takes four steps occasionally takes twenty, and you pay for all of them.
How do we debug an agent?
Full step traces, retained. Without them, a wrong outcome is unexplainable, which is unacceptable in most business contexts.
Should we wait for agents to improve?
Do not wait to start. Build the workflows that pay now; adopt agentic patterns where they prove themselves on your own evaluation set.
Been pitched an agent-based solution?
Ask what happens when it chooses wrong. Tell us the problem and we will say whether it needs an agent or a workflow.