In the previous blog, we learned why AI agents fail when workflows are weak, even if the agents themselves look powerful. In this blog, we go one step further and unpack a new confusion many teams face: why agents still struggle when they keep running for long periods.
How long should an AI agent run Read More »
When teams start using AI, the most common assumption is simple. One strong model should be enough. It feels efficient, easier to manage, and less confusing. If the model is powerful, why not let it handle everything?
Why one AI model is not enough in real work Read More »
AI agents are having a moment. They plan tasks, reason across steps, and respond like a capable assistant. Demos look smooth. Early trials feel impressive. It is easy to think that once an agent is added, the work will take care of itself.
Then reality shows up. Tasks stall. Outputs need fixing. Humans quietly step back in. The agent is still there, but the workflow is doing most of the heavy lifting.
This is not because AI agents are weak. It is because workflows decide whether they succeed or struggle.
Why AI agents fail without strong workflows Read More »
Many Power BI issues do not start with poor visuals or weak calculations. They start much earlier, when logic is placed in the wrong layer. Reports become slow, models turn confusing, and small changes take longer than they should. The question of whether logic should live in Power Query or DAX is not technical trivia. It directly affects performance, clarity, and long-term usability.
This blog explains how to think about that decision in a practical way.
Power Query vs DAX: How to place logic correctly in Power BI Read More »
