Virtual Agent Economies (a remarkable paper by Google research team)

18 Sep, 2025

The Source: https://arxiv.org/abs/2509.10147

On September 12, 2025, researchers from Google published a rather interesting article regarding what a market for economically active AI agents should be like, and what fundamental rules the economy of this market should comply.

One of the main theses: proactive design and development of infrastructure and standardization for economically active AI agents (and they will be economically active) is the key to the success of a secure and ethical AI-agent economy. A focus on security and ethics as guarantors of the development of the AI ​​economy. In agents' economic activities, the emphasis is not only and not primarily on the consumption of resources, data, capacity, tools, etc., but also on inter-agent economic interactions. The market in this context is a hybrid ecosystem of AIs, traditional software, and humans, all interacting and transacting with one another and generating value through the virtue of this activity.

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Key Points

• The market is searching for a framework to achieve a steerable AI economy based on safe, trustworthy, and accountable socio-tech infrastructure.
• Without targeted, proactive design and development of this infrastructure, we will soon face the spontaneous emergence of an AI economy beyond direct human oversight.
• Autonomous AI agents already interact with one another to generate economic value independently of human labor.
• Autonomy is the key feature that distinguishes modern AI agents from the more specialized systems of the past, and it seems we have not yet fully grasped the depth and significance of this difference.
• The emergence of interoperability standards (A2A, MSR) makes the reality of an agent economy (the emergence of an economic layer in the AI ​​agent ecosystem) inevitable. But these standards are insufficient to govern the development of the AI ​​agent market and curb its spontaneous evolution.
• A new term is emerging: "virtual agent economy" or "sandbox economy," where AI agents operate safely within this economic layer (in the context of the approach proposed by the authors, the term is quite appropriate). Within this economic layer, agents operate safely, legitimately, ethically, and generally as they should.

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The gist of the discussion and how to approach the problem:

1. The AI ​​agent economy can emerge intentionally or spontaneously. The lack of standards and protocols makes the likelihood of spontaneous emergence higher (the machines will rise up and figure things out on their own). Conversely, the more we proactively develop standards and protocols, the clearer and more conscious the evolution of this market becomes.
2. The agent economy can be hermetic or open and interact with the "human economy," the outside world, performing actions within it.
3. We are currently moving toward a spontaneous and open AI economy. Development is uncontrolled, communication with the outside world is direct, and there are no barriers to scaling risks from the AI ​​environment to the open world.
4. To outline the technical and governance infrastructure required to safely and robustly scale agentic AI deployments

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Example scenarios where an agent economy can emerge

1. Where people can't perform at desired level, a challenge to human capabilities. Example: Accelerating science AI agents have already proven their effectiveness in this area; traditionally, scientific research was budgeted and compensated differently than it can now be for agents. Agents can greatly accelerate research, but they also consume resources, energy, and expertise from external sources.

2. Where people don't want or avoid to do something themselves. Example: Robotics Complex, dangerous, or boring and repetitive tasks that people tend to delegate also require energy, time, and resources. Under conditions of limitations on physical presence and the volume and priority of tasks, an inter-agent economy can emerge.

3. Where people need and welcome support, the benefits at the everyday level are clear. Example: Personal Assistant One of the first use cases with an agent economy. A wide range of tasks that typically require resources, a variety of tools can be used.

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What to do Google researchers coin the term "sandbox economy," which essentially means that agents operate and interact within an infrastructure that ensures their safe and ethical operation. This infrastructure defines and enforces rules for its interaction with the outside world, which can ensure "transparent" interaction between the agent economy and the human economy, or, conversely, guarantee isolation.

The primary motivation for this approach is:

• Complete human control of an active AI environment in real time is virtually impossible.
• The speed and frequency of transactions in an AI environment are inherently higher and incomparable to those in traditional economies and financial infrastructure.
• Risks and crises in a sandbox environment should not leak to the outside world.

The use of digital currencies is being considered for the economic activities of AI agents. This is done not only from the perspective of their traceability, auditability, and the performance characteristics of the infrastructure on which they operate, but also from the perspective of risk management between the traditional financial system and the AI ​​infrastructure and financial environment.

To compare the nature of possible economic activities, the example of high-frequency trading in financial assets is given. Although it exists in traditional financial infrastructure, it requires special attention to risk management. The "sandbox" approach should address this issue.

Another term worth noting is High Frequency Negotiation. This relates primarily to scenario group 3 (see above – personal assistants). If everyone has their own personal assistant-level AI agent with extensive capabilities, access to tools, etc., then transactions with the outside world, consumption, and access to resources could change globally. Competition in different consumption areas will change, and inequality may increase. This scenario also requires tools.