What we are learning
‍Building SenseLab

Today most people run coding agents on a laptop, or on one long-lived dev box the agent has been slowly redecorating for three weeks. Those are pets.

We can get agents to use tools, follow instructions, coordinate through chains and graphs. What we can't easily do is get them to collectively improve at a task over time.

Continual learning requires training signal. Training signal requires knowing what your agents read, what they decided, what happened as a result, and which pieces of knowledge were actually causal in the outcome. That's a cognitive layer.

The switching cost isn't the model anymore. It's everything the model remembered. That's a moat the vendor gets to build out of your data, and the better their memory works, the deeper it gets.

The best way to explain agent memory: Lucy wakes up every morning with no memory of the day before. She still talks. Still paints. Still knows her family. She lost something and she kept something. That split is exactly how an agent's memory works.

The moment you go from one agent to several working on the same codebase or the same production system over days and weeks, the bottleneck moves