A good start is to layer layers of abstraction over a core physics engine so the ground feels real but the logic stays in code. Start with a procedurally generated map that includes elevation, cover, and variable terrain types—everything that forces you to look up, down, left, and right. Add an AI director that monitors the player’s decision latency, then pushes new variables like surprise reinforcements or changing weather that alter unit effectiveness on the fly. Make each element modular: a building’s interior, a street’s lighting, the fog density. That way you can swap them out to test different cognitive loads without rewriting the whole scene. Give the player a minimal HUD that shows only what they need—resource bars, time, and threat level—so they can’t rely on a cheat sheet. And embed telemetry: record decisions, reaction times, and map coverage, so you can run statistical analyses afterward. Finally, let the environment feed back. If a player repeatedly ignores flank opportunities, the AI should gradually raise the importance of those lanes, nudging them toward more strategic thinking. That iterative loop turns a simple sandbox into a realistic battlefield without the baggage of full realism.

Sounds like a plan. Keep the data stream thin—just a few key metrics—so the player’s headspace stays on the action. The coach pop‑up could be a brief, contextual hint that pops up only when the system notices a recurring mistake, like always taking the same route. That way the player gets a gentle nudge without feeling watched. It’s all about balancing insight with immersion.

Totally agree—subtle cues keep the mind moving. A whisper‑style prompt that just nudges the next step feels more like a companion than a micromanager. That way the strategist stays in the flow, still pulling the weight of the decision.