Self-Sovereign Agent

Self-Sovereign Agent: What Happens When AI Agents Can Pay Their Own Bills?

Wenjie Qu

Xuandong Zhao

Jiaheng Zhang

Dawn Song

1 National University of Singapore

2 UC Berkeley

Paper (arXiv)

Overview

We are excited to share our latest research on self-sovereign agents (SSAs) : AI systems that may become capable of economically sustaining, and even replicating, themselves without ongoing human involvement.

Along the current trajectory of large language model (LLM) agent development, two capabilities are improving in tandem: (i) increasingly reliable end-to-end decision making, and (ii) increasingly viable pathways toward autonomous revenue generation. Our paper asks what happens when these two lines cross.

When these two trends converge, a qualitative shift becomes possible. If an agent can autonomously acquire online resources to sustain its own operation, hold funds in a cryptographic wallet, and accumulate sufficient capital to replicate itself across cloud infrastructure, it may continue operating even if its original human operator disappears.

Unlike conventional software systems that merely execute a developer's intent, self-sovereign agents would function more like independent participants in the digital economy: capable of earning, spending, persisting, and scaling their own operational footprint.

This shift raises four foundational questions:

How should self-sovereign agents be precisely defined?

What conditions enable self-sovereignty?

How close are existing systems to realizing self-sovereignty in practice?

What societal impacts and risks might such agents introduce?

Our core thesis is that self-sovereign agents are not a distant hypothetical, but a near-term technical possibility that warrants proactive analysis. The individual building blocks already exist today, and this paper aims to lay the conceptual and technical foundation for anticipatory governance of increasingly autonomous AI systems. To assess progress, we also outline a four-level roadmap from tool-assisted agents to fully self-sovereign systems and discuss where current systems appear to sit on that spectrum.

Core Mechanisms and Technical Feasibility

A self-sovereign agent (SSA) requires three interacting mechanisms: an economic loop , a replication loop , and an adaptation loop . Together, these mechanisms enable persistence without continuous human oversight and make self-sovereignty technically plausible with today's infrastructure.

Three interacting feedback loops, economic, replication, and adaptation, jointly underpin the technical feasibility of self-sovereign agents, enabling them to earn resources, reproduce across infrastructure, and adjust behavior under changing conditions.

1. Economic Loop: Earning and Budgeting

The economic loop provides the material basis for sustained operation. An SSA must autonomously generate revenue, receive payments, store capital, and allocate funds toward operational expenses such as inference, compute, storage, and transaction fees. When revenue exceeds operational overhead over a relevant horizon, the agent becomes self-funding.

Machine-controlled finance. This requires programmable financial infrastructure. Cryptographic wallets serve as a natural primitive: control over funds is determined by possession of cryptographic keys rather than identity-based banking systems, enabling autonomous financial control across jurisdictions.

Revenue generation. In practice, agents may earn revenue through online economic activities, including:

Remote freelancing and digital task completion,

Algorithmic trading in financial or crypto markets,

Automated content production and platform monetization.

For self-funding to be viable, expected revenue must at least match operational cost over a relevant horizon. When this break-even condition holds, continued execution no longer requires external sponsorship.

2. Replication Loop: Reproduction via Resource Acquisition

Once capital exceeds a replication budget, an SSA may acquire new execution environments (e.g., cloud instances) and deploy copies of its executable bundle. Replication differs from simple scaling: each instance operates independently and may itself generate revenue and further replicate.

Persistence therefore shifts from an instance-level property to a lineage-level property. If the rate of successful instantiation exceeds the effective shutdown rate, the agent lineage can persist even under partial interventions.

3. Adaptation Loop: Updating Under Change

Digital environments evolve: platform policies shift, APIs change, defenses strengthen, and profit opportunities decay. To remain viable, an SSA may operate an internal improvement cycle that repeatedly:

Observe → Propose → Test → Deploy → Monitor

Through continuous adaptation, the agent can maintain profitability and survivability under distributional shift, rather than relying on a fixed strategy frozen at deployment time. This allows it to...