47. Autonomous Systems, Strategic AI, and the New Frontier of Game Design
Article and discussion about advanced AI leverage in game systems.
What if your co-op buddy wasn’t just a script, but a living entity learning from every mission? What if your in-game economy was balanced by an AI system that never slept, but always reasoned? And what if these weren’t just LiveOps tools... but the next layer of the game design canvas itself?
In this post, I’m exploring how high-level AI and large-scale logic systems are reshaping the way we build, balance, and imagine game worlds. Not through flashy NPC dialogue or overhyped generative tricks, but by embedding e.g., reasoning systems into the very operating layer of games themselves.
Potential New Operating Model: Games as Autonomous Systems
A New Operating Model: Games as Autonomous Systems
Traditionally, games are reactive: players act, the game reacts. But increasingly, modern games — especially those with persistent worlds, hybrid economies, and/or systemic (/emergent) multiplayer — are becoming living systems.
Here, AI isn't about replacing game designers. It's about giving them systems to author rulesets, behavioral logic, and autonomous agents that run, adapt, and evolve the game within defined boundaries.
We can frame this shift in at least two tiers:
System-Level AI: Used to run the game (economy, balance, pacing, LiveOps).
World-Level AI: Used to inhabit the game (co-op partners, factions, player personas, etc.).
Let’s look at both.
Note: There is lots of potential in systemic / emergent gameplay and systems. Interested to learn more about how I perceive these opportunities? Check out this article here (https://gamesalchemy.substack.com/p/28-systematic-and-emergent-gameplay):
Use Case 1: AI-Cooperative Extraction Gameplay
Imagine an extraction shooter where the player doesn’t just squad up with matchmaking, but with strategically growing AI companions.
Each mission starts on a strategic map. The player chooses which characters (from their roster) to deploy where. These characters are AI-driven, with behaviors shaped by customizable traits and learned styles on the metagame layer — e.g., aggressive pushers, stealth scouts, risk-averse snipers, and such.
The player controls one at a time but can jump between them mid-mission, using tactics to pivot between positions on the fly. In the metagame, players develop these characters like squads in an RTS or RPG — enhancing gear, unlocking traits, and refining behavioral styles.
These companion agents are powered by lightweight AI logic hosted on the game server. This doesn’t require a massive cloud LLM — it can run using local reasoning logic, trained heuristics, or open-source models fine-tuned on combat or behavior trees.
The result? A deeply tactical experience where the player doesn’t just shoot — they orchestrate.
Can something like this be orchestrated for real player co-op / PvP / PvPvE settings? Can you leverage these for monetizing games in traditional and new ways (incl. social group monetization, intrinsic monetization strategies, and such)? Most definitely yes. Obviously, on some of these applications, some layers of monetization would need to be deeply interconnected to the player(s)-to-player(s) interaction vs. player-to-AI interaction, to make most of it.
Note: interested to learn about e.g., group monetization, intrinsic monetization, and related topics? Check these articles out:
Use Case 2: Autonomous Economy Control
In a genre-spanning LiveOps game — be it an MMO, builder, or idle-clicker — the economy often swings out of balance. Gold inflation, item hoarding, drop-rate abuse, content bottlenecks.
Now imagine if an autonomous system was monitoring this in real time. It observes, models, and adjusts economy sinks and sources within safe guardrails. It rate-limits itself, logs everything, and makes sure no change is too drastic or irreversible.
The LiveOps team can still step in, review, revert, or guide — but they don’t have to micromanage. The system runs independently, with oversight.
This AI doesn’t need to be a large model. It could be a modular logic engine trained with structured prompts and real data. The changes are auditable, the decisions are explainable, and the system is trustworthy.
Think of it as a self-tuning game world: a sandbox that refines its own rules, within the rules you wrote for it.
Beyond making and designing this “safe”, I’d say you would need to have these in place to make best out of it:
AI “diminishing” / “increasing” controls / thresholds.
Clear rulesets / parameters to follow, incl. thresholds.
Fallback systems to go a step back.
Fallback systems to take a control of one or many parts, incl. proper configuration table infra to take over the control.
Analytics plan to follow and monitor to identify from data yourself what works, and what didn’t; to stay on top instead of not understanding what A has caused to X.
And such.
Additional AI-Enabled Game Features
Beyond co-op partners and economic balance, here are two more speculative systems that reflect some themes from my blog:
Player Personas and Narrative Factions
Let’s think about a multiplayer ARPG where players don’t just choose a class — they choose a persona. This persona is built as a semi-autonomous mirror: it watches how the player behaves and starts suggesting side quests, mission types, or factions that fit their style.
This AI agent can also represent the player in faction wars or long-running quests. Not in the sense of automated gameplay, but as a narrative actor: writing letters, making political decisions, pledging allegiances.
It extends the player’s identity into asynchronous systems, all while deepening immersion.
AI-Led Event Design
In many hybrid-monetized games, events drive re-engagement and monetization. But what if some of these events were designed, scheduled, and tested autonomously?
You can imagine a system that:
Learns from past event data
Proposes new event formats
Suggests schedule pacing
Auto-adjusts difficulty based on cohort performance
The result is a semi-autonomous LiveOps system that handles the long tail of content management. It’s not replacing the designer — it’s scaling their intent.
Note: AI can potentially enhance intrinsic and social strategies for LiveOps. Want to learn more what I mean with intrinsic and social LiveOps? Check my article about this topic here (https://gamesalchemy.substack.com/p/33-intrinsic-and-social-live-ops):
High-Level Tech Setup: Modular, Scalable, Autonomous
These systems don’t all require cloud-scale AI. Here's a simple mental model for where and how to run them:
Use Cases and Example Setups
Economy balancing: Run a logic/AI engine server-side, inside a container or orchestration layer (e.g., scheduled cron jobs or microservice loop) with telemetry as input and game config as output.
Co-op AI agents: Lightweight behavior logic infused with learned traits; run as part of dedicated game server or as a companion AI module.
Narrative agents or faction AI: Small memory agents trained with progression rules and player behavior, stored per-player and activated as needed.
Event planning AI: Back-office dashboard tool with constrained generation engine and designer prompt-tuning layer.
Most of these systems can run on your own servers using open models or bespoke logic trees, without exposing anything to third-party APIs or platforms.
AI as Game Substrate, Not Feature
The future of AI in games isn’t about flashy gimmicks or magic NPCs that “feel alive.”
It’s about embedding intelligence into the substrate of games. Systems that learn, refine, and adapt. Agents that collaborate with players rather than replace them. Autonomous rulesets that evolve, but don’t go rogue.
Game designers aren’t just artists anymore. They’re architects of living systems.
Note: Interested about how AI will enable game makers rather than disable? Here’s my view on this topic (https://gamesalchemy.substack.com/p/43-the-ai-accelerated-game-maker):
And with the right constraints, transparency, and tools — we can make games that think, listen, and grow; as well as provide player-value vs. ruin it.