agent experience
Your agents should learn for the organization
Agents learn while doing work, but most of what they discover disappears with the conversation. Organizations need a way to turn those discoveries into verified, current knowledge.
Every team needs a knowledge interface
For years, organizations invested in intranets, enterprise search, wikis, metadata, taxonomies, knowledge graphs. None of this is new, so why does it suddenly feel urgent again? Because the audience changed.
Inference efficiency is about protecting attention
Every unnecessary step an AI agent takes spends reasoning capacity on the tooling. Inference efficiency keeps its attention on the user's problem.
We're measuring AI productivity the wrong way
When we talk about AI and productivity, we ask how much time it saved us. That's the wrong question. The one that matters is how much time it saved everyone. AI didn't remove the cost of communication. It shifted it from the author to the audience, and we're celebrating the wrong side of the ledger.
Every configuration option is a question we couldn't answer
Open your favorite AI coding agent and you're greeted with a surprising number of decisions. Which model? How much reasoning? Which MCP servers? Most of us don't know the right answers. We treat that as the cost of cutting-edge AI. It's a sign the technology hasn't matured yet.