AI Agent's 'Coming of Age': What Step Is Missing from Simulation Training to Real Trading?

With a training and feedback environment in place, and authorization and execution resolved, Web3's AI Agent can truly complete a task.

Written by: imToken

If you are a high-consumption Token user, you should have felt the core evolution of AI Agents over the past year:

Beyond the intelligence leap of the underlying large models, they are starting to resemble a real 'worker' more and more.

From the earliest chatbots to the gradually maturing infrastructures like MCP, Skills, and Harness, Agents are gaining more and more 'limbs'. They can open web pages, read real-time data, operate software, and even connect wallets and trading accounts.

However, at this stage, new real-world issues have emerged: Using tools and actually completing a work task are two different things.

A truly task-capable Agent should not just call an API once after receiving instructions; it needs to continuously adjust its next actions based on results in a constantly changing environment.

The financial market exemplifies this difference particularly well.

You can tell an Agent, 'Help me determine who is more likely to win a certain match,' and it can provide a seemingly good analysis in seconds. But if the request changes to 'With a certain amount of funds, continuously participate in the prediction market over the next month while controlling maximum drawdown to increase returns,' it becomes a completely different problem.

It must continuously read real-time news, odds, and order books, assess whether the market is fully priced, decide when to enter, increase positions, or exit, and promptly adjust strategies after its judgments go wrong.

Recently, a series of innovative infrastructures have emerged, seemingly filling in this previously missing piece of the puzzle one by one.

1. Does AI Agent Need a 'Simulation Training Ground'?

In mid-August, SKALE launched an interesting new product, AgentPit.

In simple terms, it is a 'simulation trading sandbox' specifically built for prediction market AI Agents, synchronizing real Polymarket market information and compatible with its API, while using a CLOB order book and CTF Token settlement mechanism close to a production environment, only replacing real funds with simulated USDC.

Developers can allow Agents to read market data in real-time, place orders, manage positions, and test strategies without any risk of financial loss, while also observing the collective behavior that emerges when multiple Agents compete in the same order book.

This seems somewhat similar to backtesting and simulation in traditional quantitative trading, but for AI Agents, its significance is far greater.

Because one long-term problem faced by Agents is: how do you know if an Agent 'really trades'?

It is well known that in the past, assessing the capabilities of large models heavily relied on static benchmarks—solving a few math problems, fixing a piece of code, or summarizing a long text.

However, complex decisions in the real world do not have preset standard answers, especially in financial markets. Suppose an Agent assesses that the fair value of a 'YES' prediction event should be $0.7, while the current market price is $0.55. After it buys in, the price might continue to drop to $0.45, or it might surge rapidly due to breaking news. Additionally, the instantaneous liquidity of the order book will directly affect the actual transaction average price.

This means that the Agent cannot complete the task with just one prediction; it must continuously face the real consequences of its previous decisions, which is the core value of AgentPit. It provides a dynamic feedback loop that approximates a real production environment for the Agent:

Observe → Judge → Execute → Get Feedback → Adjust → Execute Again.

Of course, 'training' here does not necessarily mean that the Agent will automatically modify the underlying model parameters after completing each transaction; it provides an environment where strategies can be run repeatedly, results evaluated, and workflows iterated.

Just as an autonomous driving system must undergo numerous extreme scenarios in a simulator before hitting the road, a financial Agent that may manage real funds cannot simply gain asset control through a few Prompt tests; it needs to first prove how it performs in a continuous decision-making environment.

But problems arise accordingly.

Performing well in a simulated environment does not mean you can immediately hand over real money to the AI; a more critical layer is still needed in between.

2. Infrastructure Alignment: When Payments and Trading Become Native Components of the Agent

If you observe the actions of mainstream cloud giants and leading trading platforms over the past few months, it is not difficult to find that the operational track of Agents is completing standardization at an astonishing speed.

First, the implementation of machine-native payment protocols.

On August 18, Amazon Bedrock AgentCore Payments officially went GA, allowing Agents running on AgentCore to autonomously discover and pay for third-party APIs, MCP services, or specialized data sources, integrating wallet infrastructures from Coinbase and Stripe / Privy, and natively supporting stablecoins and machine payment protocols like x402.

This solves a critical pain point—if an Agent finds that a high-quality real-time data source requires payment while analyzing the market, in the past, the workflow would have to pause, waiting for a human to manually swipe a card and configure an API Key before continuing.

Under the framework of AgentCore Payments, payments are directly encapsulated as an automated node within the workflow. Users only need to set a budget and strategy in advance, and the Agent can autonomously initiate micro-payments to complete calls when encountering payment barriers, with the entire process requiring no human intervention, and the wallet private keys and credentials kept isolated from the model itself.

Two days later, on August 20, Binance launched Agent OS, opening up native interfaces for Agents.

Unlike simply providing a single AI function, Agent OS is more like a financial infrastructure interface specifically for Agents, integrating Binance API, Wallet Agentic Hub, x402, Skills Hub, and MCP components.

It allows compatible AI applications to obtain market data, view account information through MCP, and execute supported trading operations after user authorization. More importantly, it emphasizes permission boundaries and isolation at the mechanism level:

Users can assign dedicated sub-accounts to Agents, isolating funds and trading activities from the main account, and configure specific permissions for Agents; related access permissions can also be revoked at any time.

When you put AgentPit, AWS AgentCore Payments, and Binance Agent OS together, you will notice an increasingly clear change:

AgentPit provides an environment for strategy testing and continuous feedback, x402 and AWS AgentCore Payments begin to connect machine-native payments, while Binance Agent OS further opens market data, accounts, trading, and on-chain capabilities to Agents, making the originally fragmented capabilities gradually string together into a complete workflow.

However, one particularly unique aspect of financial scenarios is that if the code is wrong, you can just rerun it, but if an Agent executes an erroneous trade, the assets may have genuinely left the wallet.

This means that when transitioning from a simulated environment to the real world, there is a role that cannot be bypassed.

That is the wallet.

3. What Is the True Value of the Agent Wallet?

If we outline the complete workflow of future AI Agents, it will present a clear chain:

• First, train and test strategies in a simulated environment like AgentPit;
• Then obtain information and tools through MCP, Skills, and various APIs;
• Purchase data and computing resources through protocols like x402;
• Subsequently execute trades in the real market;
• Finally, continue to optimize strategies based on actual results;

At the execution stage, the role of the wallet will be entirely different from today.

After all, the interaction logic of traditional Web3 wallets is designed for human-machine interaction—every signature relies on humans verifying visually on the screen and manually clicking to confirm. However, if a high-frequency Agent needs to read multiple sets of market data every second and continuously fine-tune positions, still requiring humans to sign each transaction, the meaning of automation will be lost.

However, if you directly hand over the private key to the AI, it is equivalent to abandoning all security bottom lines.

Therefore, what the Agent Wallet truly needs to solve is not 'how to let AI access the private key,' but 'how to provide Agents with secure and controllable execution rights without relinquishing ultimate asset control.'

This is also the core solution provided by imToken in the design framework of the Agent Wallet:

• Account Isolation and Session Keys: In imToken's Agent Wallet design concept, each Agent that gains execution rights corresponds to an independent Agent Account, with its Session Key generated and isolated in a Trusted Execution Environment (TEE) and will not leave this secure environment.
• Hard Constraints on Policy Rules: Each Agent Account must be bound to explicit Policies, including whitelists of allowed interaction protocols, single transaction limits, daily quotas, operation frequencies, and authorization validity periods. In other words, the Agent does not receive an unconstrained wallet but rather an execution account surrounded by rules.
• Users Retain Ultimate Control: The Agent can only autonomously execute within the Policy range pre-authorized by the user; operations beyond the boundary must be re-confirmed by the user, who can always adjust the Policy, pause or resume the Agent, revoke permissions, and reclaim funds; for transactions identified as abnormal or exceeding preset strategies, automatic execution will also pause and require user re-authentication.

From this perspective, re-examining the entire ecosystem reveals that simulation training (like AgentPit) and smart wallets (like imToken Agent Wallet) are actually addressing the same problem in two stages.

The former is responsible for enhancing the decision-making capabilities of Agents, while the latter is responsible for solidifying the safety bottom line of Agent actions.

Both are indispensable.

This may be the most significant change of the Agent Wallet compared to traditional Wallets.

Final Thoughts

In this sense, with the emergence of AgentPit, a new phase worth paying attention to is approaching.

AI Agents are transitioning from 'learning to use tools' to 'learning how to work.'

And when they finally step out of the simulation training ground and into the unpredictable world of real trading, whether they can walk steadily and far often depends not only on how smart the underlying large model is but also on how solid the account and authorization boundaries we build for them are.

After all, in a real and complex economic system, what we truly need is not an Agent that never makes mistakes, but an Agent whose risks remain within controllable boundaries even if it may err.

This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.