2 min lesson
Four things to do before the loop
Match each case in "Four things to do before the loop" to the signal and response that fit it.
Step 1 of 2
Four things to do before the loopEarn the opinions
Run multi-file Agent edits, Tab and @-context on an actual project for a week.
Note where the agent loops, stalls on a tool call or loses a constraint mid-session.
Try Copilot, Claude Code and Windsurf on the same tasks.
Compare latency and reliability with specifics, not a winner-takes-all verdict.
Where does the agent harness hang versus where is the model just slow?
Which failures are rare-but-catastrophic versus frequent-but-minor?
Tie every critique to a signal you could query.
"It feels laggy" becomes "p95 time-to-first-token on long sessions."
Carry one prepared point of view into the room: if I owned reliability here, the first metric I'd ship is X because Y. That single sentence is what turns a user's vibe into a data scientist's argument.
"On long agent sessions I see time-to-first-token degrade and the occasional tool-call hang where the agent waits on a terminal command that never returns. If I owned reliability, the first metric I'd ship is agent-turn success rate segmented by session length and tool type, because the pain isn't average latency - it's the tail on long sessions and that's where users rage-quit."
That survives follow-ups because every clause opens a door: which tool hangs, how often, how would you instrument turn success, how would you separate a slow model from a stuck harness. You can answer all of them because you hit the failure yourself.