Context
AI agents are powerful in a single turn and forgetful across them. The hard problem is not the model. It is the context that surrounds it, what the agent remembers, what it can reach, and how that state is shaped.
Problem
Most agent systems treat context as an afterthought, a prompt window stuffed with whatever fits. That breaks down the moment work spans more than one session or more than one file.
What I did
- Explored memory and context formats that let agents retain and retrieve relevant state efficiently.
- Worked on the plumbing between agents, files, and workflows, the unglamorous layer that decides whether agents are actually useful.
What I learned
- The model is rarely the bottleneck. The context architecture around it usually is.
- Good memory is a compression and retrieval problem before it is a model problem.
Working on agent systems reinforced a pattern I already knew from hardware. The visible component gets the credit, but the real bottleneck is somewhere quieter.
Context as the real substrate
An agent is only as good as the context it operates on. Memory, file access, and workflow state decide whether an agent is a clever demo or a dependable tool. That is where the interesting engineering lives.
This work sits close to the same instinct that drives everything else I build. The intelligence around the machine matters more than raw power.