Tech Sketch: Automation vs LLMs vs Agents

When you are choosing how to build an AI-enhanced workflow, the key is matching each tool to the type of task it handles best.

• Automation is ideal for predictable, rule-based steps, such as moving files or triggering updates.
• LLMs are better for tasks that require understanding or generating content from unstructured inputs.
• Agents work well when the process has multiple steps, unpredictable branches, or needs planning, tool use, or coordination across systems.

A simple feedback pipeline shows the difference, with automation handling collection and organization, LLMs transforming raw data into structured insights, and agents managing decisions, validations, and multi output reporting. The larger point is that each tool excels at different roles, and using them where they fit naturally creates workflows that are faster, more reliable, and easier to maintain.

Image credit: Ryan Snaadt