Working out, in writing, how parts of the agent stack actually function.
The mental model is the easy part - handle each kind of tool error where it can actually be fixed. The build is where the detail lives: telling failures apart when you can't trust their wording, retrying in the harness, and stopping a model that won't stop itself.
Tools fail constantly, and what separates an agent that recovers from one that stalls or spins isn't a smarter model. It's where you handle the error.
When an agent needs a few independent lookups, there's no reason to do them one at a time. Parallel tool calls, what the model is actually deciding, and the cheapest speed-up there is in an agent.
How the ReAct loop actually works, why mixing reasoning and acting was the real insight, and the failure modes that show up when you don't take the observation step seriously.
Why "let's think step by step" worked, why reasoning models work for the same reason, and what changes for anyone building on top.