As AI products lean more heavily into agentic capabilities, the same design challenges keep surfacing across projects. Here's a look at how we've approached one of these recurring debates: showing the work of agents, or not. An AI product becomes agentic when the model doesn't just respond to a prompt, but plans which tools to use, configures them, and decides its next steps based on the results. This additional set of process means AI products are able to do more, check their work, and thereby provide better results. The downside, though, is it can be a lot for people to take in. Whether people are using agentic products for coding, data analysis, or writing, I keep seeing the same split: some users find the agent's work overwhelming and want the interface to focus purely on results. Others say seeing that work is essential for monitoring and checking what the agent is doing. Strongly worded feedback comes in from both sides. I initially assumed this was a temporary divide.…