Why does evolve vs replace decision fail?

The most common reason evolve vs replace decision fails: Two opposite errors are equally common. The first is compulsive evolution — endlessly patching an agent that should have been retired three iterations ago, because you built it and you feel attached to it. The second is compulsive replacement — scrapping agents at the first sign of difficulty and rebuilding from scratch, losing accumulated refinements each time. Both errors come from the same root: making the decision emotionally rather than structurally.

The fix: Pick one agent you currently run — a habit, routine, decision framework, or mental model that feels sluggish or unreliable. Write two columns: 'Evolve' and 'Replace.' Under Evolve, list specific modifications you would make to restore or improve it. Under Replace, describe what a fresh agent designed for the same purpose would look like if you started today with no history. Compare the two columns. Which path produces a better agent with less total effort? Notice whether your gut reaction and your rational analysis point in the same direction — if they diverge, the sunk cost fallacy may be at work.

The underlying principle is straightforward: Sometimes you should improve an existing agent; sometimes you should replace it entirely.