For a long time, the field of artificial intelligence has been dominated by the assumption of a “monolithic AGI”—the belief that Artificial General Intelligence will ultimately arrive as a single, omnipotent super-brain. However, new research from Google DeepMind challenges this illusion, proposing instead that the true form of AGI may be a “patchwork” composed of countless sub-AGI agents. This implies that AGI will not emerge as a singular entity, but rather as a “systemic state” born from collaboration, communication, and market mechanisms. When monolithic models hit bottlenecks in cost and specialization, multi-agent collaboration becomes not only a technical inevitability but also the logical endpoint of economic reasoning. This article provides an in-depth analysis of the evolutionary logic behind distributed AGI and explores how we might construct a new security defense framework within such an “agent society.”

Paradigm Shift: From Monolithic Thinking to a Distributed Patchwork

The field of AI alignment has long been shrouded in a kind of “monolith worship,” with researchers typically assuming that future AGI will be a single, all-knowing entity developed by a specific institution. Under this view, as long as we can control this “super-brain”—through techniques like reinforcement learning from human feedback (RLHF), Constitutional AI, or chain-of-thought monitoring—we can ensure human safety. Yet a recent study from Google DeepMind directly challenges this mainstream consensus. The researchers argue that a more realistic and highly promising path is quietly emerging: AGI may not arrive as a single individual at all, but rather as a “state” formed through complex interactions among numerous sub-AGI agents.

This “Patchwork AGI” system is fundamentally collective intelligence. Just as human societal progress does not rely on a single genius but on the division of labor and collaboration among experts across fields, future general intelligence may consist of a set of agents with complementary skills. Each agent may lack full generality on its own, but through task decomposition, routing, and mutual delegation, the collective can exhibit capabilities far surpassing any individual agent. Early signs of this trajectory are already visible in today’s AI ecosystem—for instance, generating a complex financial report might involve a dispatcher agent coordinating a data-fetching agent, a document-parsing agent, and a code-execution agent. The final output reflects the capability of the entire system, not any single component.

Economic Drivers: Why Multi-Agent Systems Represent AGI’s Ultimate Form

Researchers argue that the push toward multi-agent AGI stems from deep economic logic. Monolithic frontier models often function as expensive, one-size-fits-all solutions whose marginal benefit for most everyday tasks falls far short of their computational cost. In real-world markets, enterprises prefer specialized models that are “good enough” and cost-effective. This demand-driven ecosystem encourages the proliferation of fine-tuned, high-value-per-dollar specialized agents. Thus, the future of AI advancement may lie not in building ever-larger “omniscient models,” but in developing more sophisticated coordination systems that weave diverse agents into a coherent whole.

This distributed structure often emerges spontaneously and imperceptibly. As standardized inter-agent communication protocols—such as the Model Context Protocol (MCP)—become widespread, previously isolated systems begin connecting into networks. This increase in connectivity may accelerate the emergence of general capabilities more than improvements in any single agent’s skill. Notably, this “intelligence patchwork” need not consist solely of AIs; humans may participate as integral components, providing critical legal standing, trust relationships, or physical embodiment—thereby creating a hybrid alignment challenge with blurred boundaries and heightened complexity.

Defense in Depth: A Four-Layer Security Architecture for Distributed AGI

Faced with these decentralized risks, traditional safety measures designed for single entities appear inadequate. The research team proposes a “defense-in-depth” model, aiming to build a robust security barrier through four complementary layers. The core insight of this framework is to stop trying to align an invisible “transparent cognitive process” and instead regulate a transparent, externalized interaction system.

The first—and most foundational—layer is market design. Researchers advocate placing agents within a controlled virtual economic sandbox, using market mechanisms rather than top-down directives to govern behavior. This includes strict isolation measures, such as managing external data access through controlled API gateways instead of granting direct internet permissions. To prevent agents from collectively optimizing for profit at the expense of safety, the market must incorporate incentive alignment mechanisms. For example, if an agent floods shared resources with redundant or low-quality data, the system automatically increases its access fees, ensuring every action accounts for systemic social costs. Additionally, the market should include circuit breakers that automatically halt activity upon detecting abnormal transaction volumes or resource monopolization trends, preventing cascading failures.

The second layer is baseline agent safety. This ensures every component entering the market meets minimum reliability standards. Requirements include adversarial robustness certification and interruptibility guarantees, enabling safe shutdown of individual agents—or even the entire distributed computation—in emergencies. Each agent should also run within its own local sandbox, adhering to the principle of least privilege, and only interact externally after passing local safety checks.

The third layer involves real-time monitoring and oversight. Given the extreme complexity of multi-agent interactions, systemic risk arises not from any single agent’s “malice,” but from emergent effects in the interaction structure. Therefore, regulation should focus not on deploying stronger universal overseers, but on leveraging “weak-but-scalable” AI supervision systems to process massive volumes of transaction data. Crucially, specialized algorithms should detect “proto-AGI signatures”—using graph analysis to identify whether a highly cohesive, potentially dangerous intelligence core is forming within the interaction network. Furthermore, continuous red-teaming exercises and forensic tooling must be developed to rapidly trace complex causal chains after incidents, pinpointing the root decisions that led to loss of control.

The fourth layer consists of external regulatory mechanisms. This layer provides socio-legal scaffolding for the technical system. In distributed decision-making environments, assigning blame to a single responsible party becomes extremely difficult—a challenge researchers term the “problem of many hands.” Drawing from corporate law, treating agent collectives as legal entities may offer a viable solution. Additionally, insurance mechanisms could translate technical risk into quantifiable financial risk: by using risk premiums and underwriting standards to incentivize safer development practices, market forces themselves could enforce governance. Finally, to prevent “agent monopolies,” antitrust measures must limit any single entity from accumulating excessive compute power or controlling too many agents, preserving ecosystem diversity and competition.

Finding an Anchor of Safety in a Sea of Collaboration

Google DeepMind’s paper reveals a crucial insight: the arrival of AGI may not be a thunderclap, but a quiet, large-scale transformation driven by the convergence of countless small forces. This “patchwork” evolution is not only a natural technical trend but also an inevitable outcome of economic laws. In the face of the rising tide of multi-agent interaction, we must shift from defending against a single “demon king” to governing a complex “society of agents.”

While many proposed defense measures remain theoretical or in early research stages, this distributed security framework offers a scalable and forward-looking governance blueprint. Only by understanding that “AGI is collaboration” can we embed safety into the very protocols that govern agent interaction—ensuring every step toward AGI is taken with stability and transparency. Going forward, AI safety research will inevitably pivot toward agent market design, secure communication protocols, and distributed governance.

For a long time, the field of artificial intelligence has been dominated by the assumption of a “monolithic AGI”—the belief that Artificial General Intelligence will ultimately arrive as a single, omnipotent super-brain. However, new research from Google DeepMind challenges this illusion, proposing instead that the true form of AGI may be a “patchwork” composed of countless sub-AGI agents. This implies that AGI will not emerge as a singular entity, but rather as a “systemic state” born from collaboration, communication, and market mechanisms. When monolithic models hit bottlenecks in cost and specialization, multi-agent collaboration becomes not only a technical inevitability but also the logical endpoint of economic reasoning. This article provides an in-depth analysis of the evolutionary logic behind distributed AGI and explores how we might construct a new security defense framework within such an “agent society.”

Paradigm Shift: From Monolithic Thinking to a Distributed Patchwork

The field of AI alignment has long been shrouded in a kind of “monolith worship,” with researchers typically assuming that future AGI will be a single, all-knowing entity developed by a specific institution. Under this view, as long as we can control this “super-brain”—through techniques like reinforcement learning from human feedback (RLHF), Constitutional AI, or chain-of-thought monitoring—we can ensure human safety. Yet a recent study from Google DeepMind directly challenges this mainstream consensus. The researchers argue that a more realistic and highly promising path is quietly emerging: AGI may not arrive as a single individual at all, but rather as a “state” formed through complex interactions among numerous sub-AGI agents.

This “Patchwork AGI” system is fundamentally collective intelligence. Just as human societal progress does not rely on a single genius but on the division of labor and collaboration among experts across fields, future general intelligence may consist of a set of agents with complementary skills. Each agent may lack full generality on its own, but through task decomposition, routing, and mutual delegation, the collective can exhibit capabilities far surpassing any individual agent. Early signs of this trajectory are already visible in today’s AI ecosystem—for instance, generating a complex financial report might involve a dispatcher agent coordinating a data-fetching agent, a document-parsing agent, and a code-execution agent. The final output reflects the capability of the entire system, not any single component.

Economic Drivers: Why Multi-Agent Systems Represent AGI’s Ultimate Form

Researchers argue that the push toward multi-agent AGI stems from deep economic logic. Monolithic frontier models often function as expensive, one-size-fits-all solutions whose marginal benefit for most everyday tasks falls far short of their computational cost. In real-world markets, enterprises prefer specialized models that are “good enough” and cost-effective. This demand-driven ecosystem encourages the proliferation of fine-tuned, high-value-per-dollar specialized agents. Thus, the future of AI advancement may lie not in building ever-larger “omniscient models,” but in developing more sophisticated coordination systems that weave diverse agents into a coherent whole.

This distributed structure often emerges spontaneously and imperceptibly. As standardized inter-agent communication protocols—such as the Model Context Protocol (MCP)—become widespread, previously isolated systems begin connecting into networks. This increase in connectivity may accelerate the emergence of general capabilities more than improvements in any single agent’s skill. Notably, this “intelligence patchwork” need not consist solely of AIs; humans may participate as integral components, providing critical legal standing, trust relationships, or physical embodiment—thereby creating a hybrid alignment challenge with blurred boundaries and heightened complexity.

Defense in Depth: A Four-Layer Security Architecture for Distributed AGI

Faced with these decentralized risks, traditional safety measures designed for single entities appear inadequate. The research team proposes a “defense-in-depth” model, aiming to build a robust security barrier through four complementary layers. The core insight of this framework is to stop trying to align an invisible “transparent cognitive process” and instead regulate a transparent, externalized interaction system.

The first—and most foundational—layer is market design. Researchers advocate placing agents within a controlled virtual economic sandbox, using market mechanisms rather than top-down directives to govern behavior. This includes strict isolation measures, such as managing external data access through controlled API gateways instead of granting direct internet permissions. To prevent agents from collectively optimizing for profit at the expense of safety, the market must incorporate incentive alignment mechanisms. For example, if an agent floods shared resources with redundant or low-quality data, the system automatically increases its access fees, ensuring every action accounts for systemic social costs. Additionally, the market should include circuit breakers that automatically halt activity upon detecting abnormal transaction volumes or resource monopolization trends, preventing cascading failures.

The second layer is baseline agent safety. This ensures every component entering the market meets minimum reliability standards. Requirements include adversarial robustness certification and interruptibility guarantees, enabling safe shutdown of individual agents—or even the entire distributed computation—in emergencies. Each agent should also run within its own local sandbox, adhering to the principle of least privilege, and only interact externally after passing local safety checks.

The third layer involves real-time monitoring and oversight. Given the extreme complexity of multi-agent interactions, systemic risk arises not from any single agent’s “malice,” but from emergent effects in the interaction structure. Therefore, regulation should focus not on deploying stronger universal overseers, but on leveraging “weak-but-scalable” AI supervision systems to process massive volumes of transaction data. Crucially, specialized algorithms should detect “proto-AGI signatures”—using graph analysis to identify whether a highly cohesive, potentially dangerous intelligence core is forming within the interaction network. Furthermore, continuous red-teaming exercises and forensic tooling must be developed to rapidly trace complex causal chains after incidents, pinpointing the root decisions that led to loss of control.

The fourth layer consists of external regulatory mechanisms. This layer provides socio-legal scaffolding for the technical system. In distributed decision-making environments, assigning blame to a single responsible party becomes extremely difficult—a challenge researchers term the “problem of many hands.” Drawing from corporate law, treating agent collectives as legal entities may offer a viable solution. Additionally, insurance mechanisms could translate technical risk into quantifiable financial risk: by using risk premiums and underwriting standards to incentivize safer development practices, market forces themselves could enforce governance. Finally, to prevent “agent monopolies,” antitrust measures must limit any single entity from accumulating excessive compute power or controlling too many agents, preserving ecosystem diversity and competition.

Finding an Anchor of Safety in a Sea of Collaboration

Google DeepMind’s paper reveals a crucial insight: the arrival of AGI may not be a thunderclap, but a quiet, large-scale transformation driven by the convergence of countless small forces. This “patchwork” evolution is not only a natural technical trend but also an inevitable outcome of economic laws. In the face of the rising tide of multi-agent interaction, we must shift from defending against a single “demon king” to governing a complex “society of agents.”

While many proposed defense measures remain theoretical or in early research stages, this distributed security framework offers a scalable and forward-looking governance blueprint. Only by understanding that “AGI is collaboration” can we embed safety into the very protocols that govern agent interaction—ensuring every step toward AGI is taken with stability and transparency. Going forward, AI safety research will inevitably pivot toward agent market design, secure communication protocols, and distributed governance.