ai basics
AI assistants, workflows, and agents: what changes?
An AI assistant can draft an answer. A workflow runs a defined sequence of steps. An agent can choose which tool or step to use next. The distinction matters because each added action creates something else you need to check.
Compare the same task three ways
Use meeting notes as an example:
| Approach | What it does | What you need to check |
|---|---|---|
| Chat prompt | Produces a draft action list from pasted notes | Facts, missing owners, and dates |
| Defined workflow | Receives notes, creates a draft, places it in a review queue | Every step, field mapping, failures, and duplicate submissions |
| Agent | Chooses sources or tools while working toward a goal | Tool permissions, chosen actions, stopping conditions, and the final result |
These are proposed designs, not reports of a deployed system. A label does not tell you how much autonomy a product actually has. Anthropic’s engineering guide describes the difference between predefined workflows and model-directed agents.
Ask what the system is allowed to do
Before choosing a platform, answer four questions:
- What input may it read?
- What output must it produce?
- What external action may it take?
- Who checks the result and handles failure?
“Help with support” is too broad. “Draft answers from this approved FAQ and send every draft to a reviewer” is a task with a visible boundary.
Start with the smallest useful version
If a person can paste the input and check the draft, try that first. A scheduled workflow may help when the same input arrives repeatedly. An agent may be useful when the next step varies and the system must choose among tools, but that flexibility also needs evaluation.
A model does not become trustworthy because it is called an agent. It can return an incorrect answer, invent a missing detail, choose an unsuitable tool, or repeat work after a retry. Learning from feedback is a separate mechanism; do not assume every agent updates itself from each conversation.
Use an acceptance checklist
For one sample task, record:
- The input and expected result.
- Details that must remain unknown when the input is incomplete.
- Actions that require a person’s approval.
- The response to a failed tool call or an empty result.
- The condition that stops the workflow.
If you cannot describe those checks, adding more tools makes the result harder to assess.
Build a reviewable action-list assistant, try a support draft exercise, or measure whether it helps.