AgentFlux: Privacy-Preserving AI for Onchain Finance

The flickering cursor on Sarah’s screen mirrored the restless uncertainty in her mind.

As a lead strategist for a major financial institution, she understood the immense promise of AI: automated trading strategies, dynamic portfolio optimization, real-time risk assessment.

She also understood the immutable demands of onchain finance: absolute privacy, ironclad self-custody, and data residency.

The two, it seemed, were locked in a perpetual dance of tension.

How could she leverage sophisticated artificial intelligence for their burgeoning decentralized finance (DeFi) operations without inadvertently exposing private keys, proprietary trading strategies, or sensitive client data to a distant cloud server?

It felt like being asked to whisper a secret across a crowded room, hoping no one else would overhear.

This dilemma was not just hypothetical; it was the everyday reality for institutions eager to innovate but bound by a non-negotiable commitment to security and confidentiality.

In short: Axelar has introduced AgentFlux, an open-source framework for privacy-preserving, on-device agentic AI.

This innovative solution allows institutions to execute onchain strategies powered by AI without compromising private keys or sensitive client data to cloud infrastructure.

Why This Matters Now: The Data Privacy Imperative

We are at a crossroads where the transformative power of AI meets the stringent requirements of onchain finance.

For years, the default mode for advanced AI has been cloud-centric, relying on remote infrastructure to handle intensive computational workloads.

This model, while powerful, presents a fundamental tension for blockchain ecosystems.

As Axelar notes in a December 2025 announcement, traditional cloud-based AI agents would necessitate sending cryptographic material, prompts, and contextual data to remote servers (PR Newswire, 2025).

This practice directly undermines the very cryptographic guarantees and privacy properties that drew many participants to blockchains in the first place, fundamentally breaking the core promise of self-custody.

This is not merely a theoretical concern; it is a critical operational challenge.

Institutions exploring onchain finance face strict requirements for data privacy, residency, and key custody.

Any AI system touching trading strategies, client portfolios, or compliance workflows must meticulously respect internal controls and regulatory expectations across both traditional financial systems and multiple blockchains (PR Newswire, 2025).

The introduction of AgentFlux directly addresses this growing chasm, offering a path forward where innovation does not come at the expense of security.

The Core Problem in Plain Words: The Cloud’s Privacy Paradox

The core problem is simple: where does your sensitive data live when AI is involved?

If AI agents, designed to abstract away the complexity of blockchain interaction, demand that you ship private keys, trading strategies, and client context to the cloud, you have fundamentally undermined the privacy and self-custody principles upon which blockchains are built.

It is a privacy paradox: the technology meant to secure your assets demands that you expose them.

Sergey Gorbunov, co-founder of Axelar and co-author of the AgentFlux research paper, frames this challenge clearly: Interacting with blockchains is inherently complex, and agents are here to abstract that complexity away.

But if agents require you to ship keys, trading strategies and client context to the cloud, you have broken the core promise of self-custody and privacy that blockchains were built on.

AgentFlux is about restoring those guarantees in an agent-native world: the agent runs where your keys live (PR Newswire, 2025).

The counterintuitive insight is that for AI to truly deliver on its promise in decentralized finance, it must become more localized, not more centralized.

The brain of the AI needs to reside closer to the data it is meant to protect and act upon.

What the Research Really Says: Insights from the AgentFlux Framework

The AgentFlux framework, incubated at Interop Labs in collaboration with researchers from the University of Washington, offers compelling insights into solving the privacy paradox for onchain finance.

Insight 1: On-device execution is key to preserving privacy in agentic AI.

The core finding is that AgentFlux introduces a framework where sensitive agentic workloads—including reasoning, tool orchestration, and context handling—run fully on-device.

Heavier, non-sensitive workloads can be selectively delegated to external services when needed (University of Washington / Interop Labs, 2025).

This on-device execution model ensures that proprietary information never leaves the institution’s controlled environment.

Practical Implication: This means institutions can deploy powerful AI agents for complex onchain strategies without the inherent privacy risks of cloud-based models.

It opens the door for wide-scale institutional adoption of agentic AI in DeFi.

Insight 2: Decoupled AI architecture enhances local processing efficiency.

Rohan Kadekodi, lead author of the AgentFlux paper, highlights a crucial architectural innovation: AgentFlux demonstrates that you do not need to ship every token to a frontier cloud model just to orchestrate tools correctly.

By splitting the problem into classification and argument generation, and giving each its own optimized adapter, agents can run locally, behaving much like top-tier cloud systems without exposing sensitive data (PR Newswire, 2025).

This specialized architecture significantly improves the viability of on-device AI.

Practical Implication: Organizations can achieve high-performance AI capabilities locally, reducing latency and reliance on external services for critical decision-making.

This approach addresses concerns around both data privacy and computational efficiency.

Insight 3: A unified, governed agent across multichain environments is the future.

Sergey Gorbunov emphasizes that institutions seek a single, governed agent capable of overseeing their exposure, reasoning about risk, and acting across the entire onchain universe.

He states, Axelar provides the multichain rails; AgentFlux provides the privacy-preserving brain that can sit inside an institution’s perimeter.

Together, they form a blueprint for how onchain finance will actually operate at scale (PR Newswire, 2025).

This integrated approach is vital for institutional DeFi.

Practical Implication: This vision suggests a future where fragmented blockchain ecosystems are seamlessly connected by intelligent, privacy-preserving AI.

Institutions can develop comprehensive strategies that span various chains without managing a multitude of disparate AI solutions.

This capability addresses the need for secure multichain interactions.

A Playbook You Can Use Today: Integrating Privacy-Preserving AI with Confidence

For financial institutions and developers ready to embrace agentic AI in the onchain world, here is a practical playbook for weaving generative AI into your operations with confidence:

To integrate Privacy-Preserving AI with confidence, financial institutions and developers should first prioritize on-device AI for sensitive workloads.

This involves explicitly defining sensitive data and operations and opting for frameworks like AgentFlux that enable on-device processing of reasoning, tool orchestration, and context handling, keeping private keys and strategies within your perimeter (PR Newswire, 2025).

This aligns with the principles of blockchain privacy.

Next, a thorough understanding of the AgentFlux Architecture is crucial, familiarizing oneself with decoupled fine-tuning, LoRA adapters, and the classification/argument generation split.

This technical insight will help optimize local agent performance without compromising data (University of Washington / Interop Labs, 2025).

Furthermore, leveraging multichain connectivity is essential for scalable onchain operations.

Pairing your privacy-preserving AI brain with robust multichain rails, like those offered by Axelar, extends agent-driven workflows across various blockchain systems, enabling a unified approach for onchain finance (PR Newswire, 2025).

It is also advisable to pilot solutions with a reference demo, such as the AgentFlux demo built on Ethereum with a Coinbase tool-calling environment, providing a practical, end-to-end example.

Engaging with the open-source community is another vital step, as AgentFlux is a community-driven framework, reflecting a philosophy of collaborative development.

Sergey Gorbunov notes that the goal is to give the community a solid, research-backed framework and a live demo that proves what is possible, encouraging developers and institutions to take it in directions yet unimagined (PR Newswire, 2025).

Finally, institutions must establish clear data residency and key custody policies.

AgentFlux is designed with these strict institutional constraints in mind, ensuring cryptographic material and sensitive client data remain on-device or within institution-controlled environments (PR Newswire, 2025).

Risks, Trade-offs, and Ethics: Navigating the AI Crossroads

While AgentFlux offers a compelling solution to the privacy challenges of AI in blockchain, implementing any advanced financial technology stack requires careful consideration of risks and ethical implications.

The primary risk lies in the complexity of managing on-device AI agents within a decentralized and constantly evolving financial landscape.

Ensuring the integrity and security of local environments, particularly when delegating non-sensitive tasks, requires robust internal security protocols.

A trade-off might emerge in the initial setup and maintenance complexity of on-device solutions compared to simpler, albeit less private, cloud-based alternatives.

However, for institutions, this trade-off is often justified by the paramount need for AI governance and security.

Ethically, the deployment of autonomous AI agents in finance demands rigorous oversight.

Questions around accountability for agent-driven decisions, the prevention of algorithmic bias, and transparency in agent behavior must be addressed.

Mitigation strategies include comprehensive auditing of agent logic and outputs, strict access controls to local environments, and a clear human-in-the-loop mechanism for critical decisions.

Incremental deployment, starting with lower-risk functions, allows for continuous learning and refinement of both technical and ethical safeguards.

Tools, Metrics, and Cadence: The Operational Pulse of Enterprise AI

Successfully deploying on-device AI for onchain finance requires a clear operational framework.

The technology stack for this approach includes a data foundation on institution-controlled hardware or environments, the AgentFlux open-source AI framework available on GitHub, and Axelar network for blockchain integration providing multichain rails.

Specific implementations demonstrated include Ethereum and a Coinbase tool-calling environment for a reference demo.

Key Performance Indicators (KPIs) for AI Success:

Privacy Compliance Rate: This metric measures the percentage of AI operations fully compliant with data residency and key custody policies, with a target range of 100%.
On-Device Processing Ratio: This KPI tracks the percentage of sensitive AI workloads executed locally, aiming for a high target, for example, greater than 95%.
Agent Autonomy Levels: Defined and measured levels of independent decision-making by AI agents are progressively increased within safe limits.
Transaction Efficiency: This measures the speed and cost-effectiveness of agent-driven onchain transactions, with a goal of optimization.
Developer Engagement (for open source): This tracks the number of contributions, forks, and custom deployments of AgentFlux, with a target of increasing engagement.

The recommended review cadence for managing these systems involves:

Weekly: Review of agent logs for anomalous behavior and security audits of on-device environments.
Bi-weekly: Developer syncs for open-source contributions, bug fixes, and feature discussions.
Monthly: Performance review of AI agents against defined KPIs and internal compliance checks.
Quarterly: Strategic assessment of AI initiatives in onchain finance, including evaluation of new Large Language Models (LLMs) for integration.
Annually: Comprehensive security audit, ethical framework review, and long-term roadmap planning for AI in finance.

FAQ

Q: What is AgentFlux?

A: AgentFlux is a new open-source framework developed by Axelar and Interop Labs that enables privacy-preserving, on-device agentic AI for institutional onchain finance, preventing sensitive data like private keys and trading strategies from being sent to the cloud, as announced on 2025-12-04 (PR Newswire).

Q: How does AgentFlux ensure privacy?

A: AgentFlux’s architecture ensures that the most sensitive parts of agentic workloads—reasoning, tool orchestration, and context handling—run entirely on-device or within institution-controlled environments.

Only anonymized or aggregate outputs might leave the local environment if necessary (PR Newswire, 2025).

Q: Who developed AgentFlux?

A: AgentFlux was incubated at Interop Labs, the initial developer of the Axelar network, in collaboration with researchers co-authoring the AgentFlux research paper, including Axelar co-founder Sergey Gorbunov and academics from the University of Washington (PR Newswire, 2025).

Q: What is Axelar’s role alongside AgentFlux?

A: Axelar, as the Gateway to Onchain Finance, provides the multichain rails that allow institutions to extend private, agent-driven workflows powered by AgentFlux across various blockchain systems and traditional financial systems, enabling a unified, governed agent (PR Newswire, 2025).

Q: Can AgentFlux integrate with traditional financial systems?

A: Yes, while designed for onchain finance, AgentFlux is built with the understanding that institutional workflows often span both traditional financial systems and multiple blockchains.

Its framework allows carefully scoped interactions, respecting internal controls and regulatory expectations across these diverse environments (PR Newswire, 2025).

Glossary

Agentic AI: AI systems capable of multi-step reasoning and autonomous task execution within a given environment, reducing human intervention.
Blockchain Privacy: The assurance that sensitive information and cryptographic material within blockchain transactions and operations remain confidential and secure.
Decentralized Finance (DeFi): Financial services and applications built on blockchain technology, operating without traditional intermediaries.
Multichain: Refers to systems or networks designed to operate across multiple independent blockchain networks, enabling interoperability.
On-device AI: Artificial intelligence processing that occurs locally on the user’s hardware rather than relying on remote cloud servers, enhancing privacy and security.
Onchain Finance: Financial activities and operations conducted directly on a blockchain, encompassing trading, lending, and asset management.

Conclusion

The flickering cursor on Sarah’s screen no longer signals restless uncertainty.

Instead, it reflects the quiet confidence of a system where innovation and integrity walk hand-in-hand.

The introduction of AgentFlux by Axelar is not just a new framework; it is a profound commitment to the future of onchain finance, one where the powerful capabilities of AI are harnessed not by compromising privacy, but by fortifying it.

It is about building the intelligent brain right where the keys live, giving institutions the self-custody and control they demand.

For developers and financial leaders, this is more than an opportunity; it is a blueprint.

This is the moment to build, to secure, and to truly empower the next generation of financial infrastructure.

References

PR Newswire. (2025). Introducing AgentFlux, a Privacy-Preserving AI Framework for Onchain Finance. https://www.prnewswire.com/news-releases/introducing-agentflux-a-privacy-preserving-ai-framework-for-onchain-finance-302632526.html
University of Washington / Interop Labs. (2025). AgentFlux research paper. https://www.agentflux.xyz/paper

Introducing AgentFlux, a Privacy-Preserving AI Framework for Onchain Finance