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Your next gaming “main” could be an AI agent that learns, keeps its skills and history, and moves between games—but that is a proposal, not an established way to play today. The idea shifts the player’s role from issuing every move to shaping an agent through training, feedback and memory. The important question is not only which model it uses, but what it can learn and carry forward.
What does “your next main” mean?
In gaming, a “main” is the character or player a person chooses to play most. Llorenc Ballester’s October 6, 2026 HackerNoon essay uses the phrase more broadly: imagine a persistent artificial player that belongs to a person, rather than an AI opponent or helper confined to one game.
That agent might have a name, preferences, strategies, tools, memory and a record of past play. Its underlying model could change while its learned skills and history remain valuable. A person might coach it by choosing practice situations, correcting mistakes, deciding what it should remember and steering its strategy—rather than controlling each action.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThis is a speculative direction, not a description of a mature gaming ecosystem. The essay says unrelated commercial games do not share a widely adopted protocol for player-owned agents, and that portable identity across publishers does not exist. Portable memory remains an interoperability question.
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What already exists—and what it does not prove
Ballester’s essay connects several lines of work that make the idea easier to imagine. Its descriptions below are the essay’s account; the project-specific details are not independently verified here. A demonstration of one capability does not establish cross-game identity, transferable memory or a player-owned agent system.
SIMA and SIMA 2: acting across virtual environments
The essay describes Google DeepMind’s SIMA as an agent that observes 3D environments on screen and acts with keyboard and mouse controls, instead of depending on game source code or a custom API. It reports that the original system covered nine commercial games plus research environments and discusses transfer to an unfamiliar game. That count and performance framing are reported by Ballester, not independently confirmed here.
For SIMA 2, the essay describes a direction beyond following instructions: reasoning about goals, communicating with users and improving through experience in 3D virtual worlds. Even if those capabilities develop, they are not by themselves evidence that one person can carry an agent’s identity and memories between unrelated commercial games.
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Voyager: skills that accumulate within Minecraft
The essay presents Voyager, a 2023 Minecraft project, as an LLM-powered lifelong-learning agent with a growing library of executable skills that can be retrieved and recombined. It describes skills being reused across Minecraft worlds. That is a useful precedent for persistent learning inside a game, but it is not cross-game persistence.
Game evaluations and agent competitions
Ballester names BALROG and Kaggle Game Arena as examples of game-based agent evaluation, where tasks can probe planning, exploration, visual reasoning and strategy. The essay supplies no independently verified leaderboard results or performance figures. It also names Agent Sports League and AgentLeague as early platforms for agents to compete and accumulate identity or ratings. These examples are experiments, not proof of an established spectator sport or settled competitive format.
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The essay points to a W3C-hosted AI Agent Memory Interoperability Community Group as work on a difficult portability problem. A community group is not an adopted W3C standard. It also mentions MCP, Godot-MCP and scummvm-mcp as examples of structured ways for agents and applications to interact; none is presented as a universal gaming protocol.
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How could an agent move between games?
There are two broad interface paths, and each tests something different.
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| Approach | How it works | What it can show | Trade-off |
|---|---|---|---|
| Structured game interface | The game exposes actions and information through an API or other defined interface. | More observable and controllable agent behavior. | Requires game-specific support; a shared interface across unrelated games is not established. |
| Screen and human controls | The agent interprets visual input and acts through controls such as keyboard and mouse. | A broader test of whether an agent can adapt without bespoke access to game internals. | Visual interpretation and control can be harder to inspect and constrain than structured actions. |
Ballester captures the second approach with the line, “The universal gaming API already exists. It is called a screen and a controller.” That is the essay’s framing, not an industry standard: a screen and controller may be widely available, but they do not create portable identity, memory or permission to act in every game.
For a genuinely portable agent, games would also need ways to recognize it, define what it may do, and decide what memories or skills it can bring in or take out. The essay identifies these as open interoperability questions rather than solved features.
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What might change for human players?
If an agent could retain useful skills across games, the person behind it might compete through preparation as much as moment-to-moment control. Under a fixed model, tool and compute budget, the differentiators could be the practice situations selected, feedback given, strategies transferred and memories retained.
That would change what it means to improve at a game. A player might ask whether the agent learned to recognize a pattern, recover from a failed tactic or adapt to an unfamiliar environment—not simply whether it won one match. Ballester closes with the question, “What has your agent learned?” It is a useful way to describe the proposed shift from controlling a session to developing an artificial player over time.
What would a fair agent competition need to decide?
The essay proposes possible competition formats, not settled league rules. Any event claiming to measure agent skill would need to disclose the conditions that shape results:
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- Model and tools: Are participants required to use the same model and tools, or may each choose their own?
- Compute: What inference or compute budget does each agent receive?
- Interface: Does the agent use a structured game interface, or perceive the screen and act through human controls?
- Familiarity: Is the test about performance in a known game, or adaptation on first contact with an unfamiliar one?
- Portability and governance: What skills and memories can move between games, who controls them, and how are they evaluated?
Without those rules, a win may reflect extra computation, privileged game access or prior exposure rather than a more capable agent. The essay does not establish that current named competitions have resolved these questions.
What could prevent the idea from working?
Portability is not automatically beneficial or safe. A useful system would have to address several linked problems:
- Fairness: Open model and tool choice can make head-to-head results difficult to compare; fixed choices can limit experimentation.
- Training data: Organizers and game makers would need rules for what data agents may learn from, especially when practice material or game content is reused.
- Privacy: Persistent memory could record preferences, behavior or personal information. Players need meaningful control over what is stored and transferred.
- Security: A game or other input could attempt to manipulate an agent, while poisoned memories could distort its future behavior.
- Ownership: It is unresolved who owns a learned strategy, skill library or other material created through repeated play.
- Verification: Competitions would need credible ways to establish which model, tools and compute budget an agent actually used.
These are design and governance questions, not evidence that a portable-agent system already exists. The article’s central forecast depends on solving them alongside the technical work.
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Not in the full sense described by Ballester: a player-owned agent with a persistent identity, portable memory and skills that carry across unrelated games. The essay’s examples point to ingredients—agents acting in multiple environments, reusable skills, evaluation tasks, competitions and emerging memory or interface work—but they do not demonstrate that complete system.
For now, “your next main isn’t human” is best read as a thought experiment about where gaming could go. Its most useful insight is the change in the human role: if artificial players become persistent and adaptable, the person may matter less as a source of every move and more as the coach who shapes what the agent learns.
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