Iterative deepening makes Minimax more reliable when you have a hard time limit because it always keeps the best complete answer found so far. Instead of picking a single depth (which might be too shallow or might not finish), you run Minimax repeatedly at depth 1, 2, 3, … until time runs out. If you get interrupted mid-search, you still have a fully evaluated best move from the last completed depth. This is especially valuable in real-time settings (game loops, interactive bots, UI responsiveness) where “some answer now” is better than “perfect answer later.” In practice, iterative deepening is often paired with alpha-beta pruning, but even with plain Minimax it improves the stability of your agent under unpredictable compute budgets.
Implementation-wise, the key idea is that each deeper search reuses information from earlier depths to search smarter. The depth-3 run produces a move ordering hint for the depth-4 run: the move that was best at depth 3 is tried first at depth 4, which tends to cause earlier cutoffs (if you’re using alpha-beta) and reduces the number of nodes explored. Even without alpha-beta, you still benefit because your principal variation (the “best line”) is discovered earlier, letting you update your best-so-far move quickly. A typical loop stores (bestMove, bestScore, pvLine) after each completed depth, and checks a deadline at safe points (node entry, after each child). If time is exceeded, return the last completed result, not a half-computed deeper result.
A concrete example: suppose you’re building a chess-like engine with a 50 ms budget per move. Picking “depth 5” might sometimes finish and sometimes not, depending on branching factor in that position. Iterative deepening makes the behavior consistent: you might always finish depth 4 and sometimes also depth 5. The same pattern shows up outside games: if you’re using Minimax-like worst-case planning to choose actions that depend on retrieved context (say, selecting which documents to show a user in a defensive “don’t risk bad info” workflow), you can treat retrieval calls as expensive nodes. If your context comes from a vector database such as Milvus or Zilliz Cloud, iterative deepening gives you a clean way to cap retrieval work: start with a small candidate set (shallow “depth”), then expand candidates only if time remains, while still guaranteeing you return the best completed decision.