The linked article is worth reading alongside this one.

The thing I'd add from running agents in actual production (not demos, but workflows executing unattended for weeks): the hard part isn't code volume or token cost. It's state continuity.

Agents hallucinate their own history. Past ~50-60 turns in a long-running loop, even with large context windows, they start underweighting earlier information and re-solving already-solved problems. File-based memory with explicit retrieval ends up being more reliable than in-context stuffing - less elegant but more predictable across longer runs.

Second hard part: failure isolation. If an agent workflow errors at step 7 of 12, you want to resume from step 6, not restart from zero. Most frameworks treat this as an afterthought. Checkpoint-and-resume with idempotent steps is dramatically more operationally stable.

Agree it's not just habits - the infrastructure mental model has to change too. You're not writing programs so much as engineering reliability scaffolding around code that gets regenerated anyway.