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What does it mean for an AI agent to become your “main”?
In gaming, a “main” is the character or role a player returns to and develops expertise with. Ballester uses the idea to imagine something broader: an artificial player that belongs to a person rather than to one game. It might carry a name, a record of past play, reusable skills, strategies and preferences, then draw on that accumulated experience in different environments.
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That is different from asking a game’s built-in AI to control a character for a match, or prompting a model to try a single task. The imagined agent would persist between sessions and potentially between games. Its identity and learned history—not just the particular AI model running it at the moment—would be what the player invests in.
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This is a proposal, not a description of a cross-game system people can currently take for granted. The essay says unrelated commercial games do not share a widely adopted protocol for player-owned agents, and that portable identity across publishers is not in place. Carrying memory reliably between games remains an interoperability question too.
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What existing projects suggest—and what they do not establish
Ballester brings together several lines of work to make the idea imaginable. They point to useful capabilities, but none, as described in the essay, demonstrates a mature system that gives players one persistent agent across commercial games.
| Example cited in the essay | What it is described as exploring | What it does not establish |
|---|---|---|
| SIMA | A Google DeepMind agent acting in multiple 3D environments from screen observations and keyboard-and-mouse actions, rather than relying on a custom interface for each game. | A player-owned identity or memory that transfers across unrelated commercial games. The essay reports coverage of nine commercial games plus research environments; that count is the essay’s report, not an independently confirmed figure here. |
| SIMA 2 | A direction the essay describes as moving beyond following instructions toward reasoning about goals, communicating with users and improving through experience in 3D worlds. | A general-purpose gaming companion with established cross-game portability. This description is attributed to Ballester’s essay. |
| Voyager | A 2023 Minecraft project described as using a language model and a growing library of executable skills that an agent can retrieve and recombine. | Persistence across different games. Reusing skills between Minecraft worlds is not the same as carrying an agent’s skills into unrelated titles. |
| BALROG and Kaggle Game Arena | Game-based settings the essay names for evaluating agents on tasks involving areas such as planning, exploration, visual reasoning and strategy. | A confirmed performance ranking or proof that agents are ready for a player-facing league; the essay supplies no independently verified leaderboard figures. |
| Agent Sports League and AgentLeague | Early competition platforms the essay says let external agents compete and accumulate identity or ratings. | Settled league rules or a mature spectator sport. The essay itself presents these as early experiments. |
The distinction matters: an agent demonstrating a skill in an environment is not automatically an agent that can take that skill, its identity and its history into another publisher’s game. Those are separate technical and governance problems.
How could an agent interact with different games?
The essay sketches two broad interface paths. Each makes different trade-offs between control, observability and generality.
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| Interaction path | How it works | Potential advantage | Unresolved trade-off |
|---|---|---|---|
| Structured game interface | A game exposes defined actions or data through an API or another machine-readable interface. | The agent’s inputs and the game’s responses can be more explicit and inspectable. | Each game may need to provide or support an interface. There is no widely adopted, universal protocol for player-owned agents across unrelated commercial games, according to the essay. |
| Screen and controller | The agent interprets what appears on screen and acts through controls analogous to a person’s keyboard, mouse or controller. | It can test whether an agent adapts without needing a custom game API for every environment. | Visual interpretation and control can be difficult to evaluate and constrain, and performance in one title does not guarantee reliable transfer to another. |
Ballester’s line, “The universal gaming API already exists. It is called a screen and a controller,” is a concise expression of the second approach—not an industry standard or a claim that screen-based agents already work reliably in every game.
What would change for the human player?
If a persistent agent became practical, the player’s role could shift from directing every move to shaping how the agent learns. That could mean choosing practice situations, giving feedback, deciding what it should remember and helping it develop strategies that transfer to unfamiliar situations.
In that model, the competition is not only between the agents. It may also be between the people who coach or configure them. Under a fixed model, tool and compute budget, thoughtful feedback and useful training situations could matter as much as the starting model. That is a proposed competitive dynamic, not a settled result.
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The central question would become less “Which model did you pick?” and more “What has your agent learned?” A portable skill library or useful memory might retain value even if the underlying model changes. But that value depends on whether skills can be represented, transferred and used safely across environments.
What would a fair agent competition need to decide?
There is no settled rulebook in the essay’s account. A competition would need to define what participants can bring and what counts as a comparable test. Without those choices, a win might reflect greater compute, privileged game access or a purpose-built interface rather than better adaptation.
- Model and tools: Are all competitors using the same model and tool set, or can each team choose its own?
- Compute limits: How much inference or other computing time may an agent use, and how is that limit enforced?
- Game interface: Do agents use structured game APIs, screen-and-controller interaction, or separate divisions for each?
- Familiarity versus adaptation: Is the test about performance in known games, or about learning a game the agent has not encountered before?
- Portable memory and skills: Can participants carry prior experience into the event, and what must they disclose about how that experience was acquired?
- Governance and verification: How are data rights, memory privacy, security and the agent’s model and tools handled or audited?
These are dimensions for possible formats, not rules that the early platforms named in the essay are known to have adopted.
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What are the hardest problems beyond game skill?
Fairness and evidence
Agents can differ in model access, tool access, training history and computing budget. To compare them meaningfully, organizers would need clear limits and a credible way to verify which model and tools were used. Otherwise, a leaderboard could reward resources or hidden preparation more than transferable ability.
Memory, ownership and privacy
A memory that follows a player might contain preferences, behavior and information from past sessions. Players would need control over what is stored, where it travels, who can inspect it and how to erase it. The essay mentions a W3C-hosted AI Agent Memory Interoperability Community Group, while emphasizing that a community group is not an adopted W3C standard. Its existence does not itself settle how game agents should exchange or govern memory.
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Persistent memory creates a potential attack surface: malicious instructions could be placed in material an agent later reads, or its stored history could be manipulated. Game operators would also have to decide whether agents are permitted, what actions count as fair play and how to prevent them from gaining an advantage unavailable to human participants.
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Skills across environments
A skill that works in one game may rely on its controls, rules or assumptions. Reusing it elsewhere could require translation, testing or substantial relearning. The ability to save executable skills, as described for Voyager, is a useful precedent for skill reuse within its context—not evidence that skills will transfer cleanly between different games.
Is this a real gaming trend already?
There are real research and evaluation efforts around agents in games, along with early competition concepts and work on agent interfaces and memory interoperability. But the evidence Ballester assembles supports a direction to watch, not the claim that persistent cross-game players are already a normal part of gaming.
The essay mentions MCP and interfaces such as Godot-MCP and scummvm-mcp as examples of structured ways for agents to interact with applications. Such interfaces do not amount to a universal gaming protocol. Likewise, a research agent acting across environments, or a competition where agents accumulate ratings, does not prove that players can carry a unified identity and private memory between commercial games.
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For now, the useful distinction is between agents that can play games and an agent that belongs to a player and keeps learning across games. The first idea has concrete experiments behind it; the second remains a vision whose technical standards, fair-play rules and player protections are unresolved.
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