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The Nansen AI agent brings integrated trading to Solana and Base networks

by ICN.live
January 22, 2026

Amira Khalil

Key Points

• Integrated analysis and execution now live on Solana and Base with a unified workflow
• Nansen connects on-chain analytics to trade execution, with user approvals and rule controls active
• Partners include Jupiter, OKX DEX, and LI.FI, with a self-custodied wallet by Privy
• Access rolls out to eligible users, with restrictions in several jurisdictions confirmed

The Nansen AI agent launches with a focus on unifying research and execution for traders.

Although the Nansen AI Agent is intended to make it easier for on-chain traders to get actionable intelligence to inform their trading decisions and to eliminate unnecessary friction at the same time, it does so by providing a single place for all of the key functions required by an on-chain trader to complete their daily activities. This means that instead of having to log into different dashboards, explore blockchains, and manage multiple wallets, a trader can now find all of the information they need to conduct their business in one place.

As such, users can obtain all of the necessary information to complete a trading activity (insights, approvals, and settlements) within the same platform, which reduces the amount of time spent navigating different platforms during high volume and/or volatile market conditions.

Traders Retain Full Control over Accounts

In addition to reducing the amount of time a trader spends completing a trading activity, Nansen has also built-in controls that ensure the trader retains full control over their account and the decisions they make regarding how to manage their money. The controls include setting rules to guide the trader’s activity and the requirement for the trader to confirm every transaction prior to being executed. By doing so, Nansen ensures that a trader’s intent remains transparent and that the trader can take full responsibility for their own financial decisions, which many traders value in terms of maintaining their security during times of extreme volatility in the markets.

Additionally, Nansen provides the transparency needed to understand why the AI trading agent is suggesting a particular course of action. Users will be able to see the reasoning behind the suggestion provided by the agent, view the wallet labels, token flows, and liquidity snapshots associated with the suggested strategy before executing any orders. This type of transparency will provide both experienced trading desks and new entrants into the space with the clarity they need to be successful in a manner that is guided by responsible safeguards.

The partners to Nansen provide additional support in terms of expanding the breadth of coverage, as Jupiter aggregates liquidity from the Solana ecosystem, and OKX DEX provides efficient execution of base blockchain swaps. Additionally, LI.FI maintains route discovery across the various blockchain ecosystems, which will enable the AI trading agent to identify entry points that require specific asset locations.

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THE NANSEN AI AGENT MOVES FROM SIGNALS TO ORDERS

The Nansen AI agent is part of a larger trend in the development of AI assistants used to assist with operations in cryptocurrency trading. Many exchanges and protocols have developed tools that utilize software agents to automate repetitive tasks related to the operation of trading and other back-office functions. However, Nansen enters the marketplace with a unique data advantage, due to the fact that their labeled wallets provide the context behind large amounts of transactional flow activity.

For example, when a fund builds a position in a particular asset, the labeled activity of the fund provides the context for leaderboards and alerts in the user interface. In turn, the user can leverage the context provided in the labeled activity to create an entry or exit in the respective asset, with the AI trading agent assisting with the decision-making process.

According to Alex Svanevik, CEO of Nansen, the current release represents the largest product release in the history of the company. He stated that the strong signals for investors enabled the release of the mobile product, followed by the release of direct execution. “We’re now closing the loop,” Svanevik said, “by allowing users to execute trades directly in our product.”

PARTNERS, POLICY, AND PROTECTIONS SHAPE USER EXPERIENCE

One of the primary concerns that exists regarding the adoption of automated systems, including AI trading agents, is the potential for increased risk to both traders and the protocols that exist across the various ecosystems. To address these concerns, Nansen has taken the approach of ensuring that the agent never takes custody of the trader’s assets. Instead, the agent operates with a self-custodied wallet that resides with the user and manages the user’s assets throughout the trading experience.

Furthermore, Nansen has implemented controls that allow the user to set rules to govern their trading activity, including the ability to limit the size of trades, define the types of tokens allowed, and define the number of networks available for trading. These controls help minimize errors made by traders during fast-moving markets, and record prompts help facilitate reviews by trading teams.

Research has been conducted for many years on agents that were designed to exploit vulnerabilities in contracts found in testing environments. Teams continue to analyze the results of this research and work towards implementing safer default options and improving the review processes that occur prior to approving transactions. The approach that Nansen has taken is consistent with this direction and continues to balance the convenience of using the system with the accountability that is expected by the user community.

On-chain analytics will continue to drive the trading decisions made by the trader community, and Nansen has outlined plans to expand the scope of its network support in the future. As Nansen expands its coverage, traders will likely expect to see increased levels of data density and deeper liquidity in the event that they wish to construct complex orders.

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PRACTICAL TAKEAWAYS FOR TEAMS ADOPTING AN AI TRADING AGENT

Before a team enables trading workflows utilizing an AI trading agent, they should develop clear policies for prompts, approvals, and logs. They should begin by having desk personnel read-only explore the trading dashboard, and then transition them to small trades under strict rule sets. Operations personnel should assess the reliability of their partners and the fees associated with their services, particularly regarding Jupiter, OKX DEX, and LI.FI. Risk management personnel should test the self-custodied wallet configuration, recovery procedures, and multi-device accessibility.

Analysts should establish benchmarks for alert performance based on labeled wallets versus manual research during previous market cycles. Product managers should solicit user input concerning the level of transparency, speed, and clarity of the system during busy market hours. Engineers should test the AI trading agent under stress conditions to determine how it handles failures and fallback paths. Finally, leadership should assess whether the integrated nature of the solution creates shorter time frames between receiving signals and informed trade execution.

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How does the Nansen AI agent help onchain traders move faster without losing control?

The Nansen AI agent brings research and execution into one interface across supported networks. Users explore labeled wallet flows, review liquidity, then approve each transaction with explicit confirmations. A self-custodied wallet keeps assets in user controlled accounts, which preserves security practices preferred by desks. Partners route orders, with Jupiter supporting Solana and OKX DEX supporting Base, while LI.FI handles cross chain paths. Rules define trade sizes, token lists, and allowed venues for each session. Logs support audits, while transparency features explain the reasoning behind suggestions. Teams reduce context switching, cut manual copying, and shorten the cycle from signal to action.

Which networks and partners are available today, and how do they shape execution quality?

Trading begins on Solana and Base, where deep liquidity and active communities support price discovery. Jupiter aggregates Solana venues to improve routing outcomes, while OKX DEX supports Base execution pathways. LI.FI manages cross chain routes when strategies require assets on different networks. The model suggests options, but users approve each step, which reduces execution errors during busy markets. As more networks arrive, coverage expands for multi chain strategies. This combination links onchain analytics to orders, which strengthens decision quality while preserving user control.

What security practices should teams use when adopting an AI trading agent for daily workflows?

Begin with limited permissions and strict rules around trade sizes, token allowlists, and supported networks. Enable two factor authentication for the self-custodied wallet, and test recovery flows across devices. Keep prompts and approvals recorded for later reviews, which helps teams understand decisions during stress windows. Train staff on model limits and watch for unexpected behaviors during volatile sessions. Run tabletop exercises covering outages at partners like Jupiter, OKX DEX, or LI.FI. Maintain jurisdiction checks and keep documentation updated as access policies change. These steps protect capital, reduce mistakes, and support responsible scaling of automated assistance.

How do labeled wallets and onchain analytics influence strategy design inside the Nansen environment?

Labeled wallets reveal who moves capital, when they act, and how their actions influence markets. Nansen organizes this data into signals, leaderboards, and alerts, which inform timing and sizing decisions. Traders compare flows from funds, market makers, and communities against liquidity snapshots and trend charts. The AI trading agent then proposes entries or exits, while users review evidence before approving. This loop helps teams test ideas quickly, measure outcomes, and refine rules for better performance. With Solana trading and Base blockchain coverage live, users align research, approvals, and settlement without leaving the application.

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