Hey, have you ever stopped to think about what slows you down when working with AI agents or other automated workflows? It’s not always the complex model-building or high-level strategy that eats up our time. Often, it’s the smaller, repetitive tasks that we overlook.
I’ve worked with AI engineering teams for years, and I’ve noticed a consistent pattern. Most of the time isn’t spent on the model itself, but on the workflow steps that surround it. Tasks like data ingestion, chunking, metadata alignment, and JSON validation can be tedious and time-consuming. These steps may not require deep technical skills, but they’re essential to keeping the system running smoothly.
So, what are the repetitive parts of your AI workflow that slow you down the most? Is it the data cleanup, the eval setup, or something else entirely? Let’s take a closer look at some of the common time-wasters in AI workflows and see if we can find ways to streamline them.
Some common examples include:
* Data ingestion: dealing with varying data formats and cleaning rules
* Chunking: simple segmentation that can break easily when inconsistent
* Metadata alignment: structural drift that requires manual fixes
* JSON validation: mechanical corrections to model output
* Eval setup: repeated patterns across every project
* Tool contracts: predictable inputs and outputs
* DAG wiring: same templates, different logic
* Logging and fallback: always required, rarely complex
By identifying these repetitive tasks and finding ways to automate or simplify them, we can free up more time to focus on the high-level strategy and complex model-building that drives real innovation in AI.