When AI Becomes a Financial Agent: Why Authorization Must Be Limited and Verification Must Be Real-Time

Compiled By Scott Shields & Stephanie Li – Contributing Writers for Capitol Times Media - From Conversations and Material Presented By California Crypto Commission’s Senior Advisor

Execution Authority, Liability Boundaries, and Verifiable Risk Control for AI Financial Agents

“AI Cannot Replace What It Depends On,” discussed a foundational logical question: AI can replace more and more human work and enter more and more execution processes, but it cannot replace external facts, attribution of responsibility, value consensus, or structures of legitimacy.

This point helps explain the theoretical foundation established in From Double-Entry Bookkeeping to Verifiable Finance: the factual foundation of verifiable finance cannot be shaken. Following this line of reasoning, finance immediately faces a more practical question: if AI no longer merely answers questions but begins to execute financial actions on behalf of people, how should it be authorized? This may appear to be a technical or product-design issue, but it is in fact a core institutional question for finance in the AI era. Once AI becomes a financial agent, it is no longer merely an information tool. It may participate in payments, trading, portfolio rebalancing, redemptions, clearing, risk control, approvals, contract execution, and asset management. At that point, the important question is not whether AI can act, but on what authority it acts, how far it may go, and who bears responsibility when it makes a mistake.

I. AI Programming Pursues Certainty; AI Agency Introduces Uncertainty

Today, many discussions of AI agents easily confuse them with traditional software. In fact, two different situations must be distinguished. One is programming with AI. AI helps humans write code, but the final product remains an app, an interface, a workflow, or a tool. Although AI is used in the development process, the goal remains certainty: the input is defined, the rules are defined, and the output should be as predictable as possible. In this case, AI is mainly a development tool; what ultimately runs is still a program. The other is AI agency. AI is no longer merely helping to develop a program; it directly executes tasks on behalf of the user. It calls tools, accesses accounts, judges circumstances, selects paths, and produces outcomes based on the user’s objectives. Many current AI agents still resemble functional apps, such as travel-booking agents, email agents, customer-service agents, scheduling agents, and trading assistants. Their functions are relatively narrow and their boundaries relatively clear; the interface has merely shifted from buttons to conversation. But if AI agency continues to develop, the problem will change. AI will increasingly resemble a human agent: it will not merely execute fixed functions, but adjust its path according to objectives, compare options during execution, modify strategies, call new tools, and even iterate on itself. At that point, its uncertainty will rise significantly. Human agents can adjust flexibly during execution because they operate within a structure of responsibility. A person bears legal, property, professional, reputational, and relational responsibilities. If an AI agent also acquires this kind of flexible execution capacity without a corresponding responsibility structure, we cannot continue to use the authorization model designed for human agents.

II. Humans Can Be Fully Authorized;

AI Cannot Be Fully Authorized Traditional society emphasizes the alignment of responsibility, authority, and interests. When a person obtains a certain agency authority or operational authority, that person normally assumes corresponding responsibilities and enjoys corresponding interests. An entrepreneur has operational authority while also bearing losses and bankruptcy risk. A fund manager has investment decision-making authority while also bearing fiduciary and professional responsibilities. A judge has adjudicative authority while also bearing institutional responsibility. An agent receives authorization, but is also bound by law and contract. AI is different. AI can be granted execution authority, but it is neither a complete rights-bearing subject nor an ultimate responsible subject. It does not possess real property interests, does not bear ultimate compensation liability, cannot go bankrupt, cannot go to prison, and cannot lose professional reputation and social relationships in the way humans do. It can execute, but it cannot ultimately bear consequences. It can operate, but it cannot absorb the consequences. It can surpass humans in certain local capabilities, but it does not possess the same structure of responsibility, authority, and interests as a human being. Therefore, it is inaccurate to say that AI “has rights” or “has authority” in the human sense. A more precise formulation is: humans or legal persons possess rights; humans or legal persons grant authorization; AI is granted execution authority. What AI receives is execution authority, not the status of a responsible legal subject. This distinction is crucial. Human agents may, under certain conditions, be fully authorized because they can bear the consequences. AI financial agents are different. AI can be granted execution authority, but it cannot be granted unbounded execution authority. It does not bear ultimate responsibility and does not enjoy ultimate interests. Therefore, it cannot be fully authorized in the same manner as a human agent. The basic principles for AI financial agents should therefore be: AI may execute, but it cannot execute with full discretion; AI may be authorized, but the authorization cannot be vague; AI may operate automatically, but it cannot expand its own authority; AI may assist decision-making, but responsibility must return to a human or legal person.

III. Authorization Must Be Limited, Clear, and Revocable

If AI agents enter the financial system, authorization cannot remain at the level of a simple “I agree” or “manage my assets well.” Such vague authorization is already risky for human agents, and it is even riskier for AI agents. AI executes faster and across a wider scope; once it exceeds its authority or makes an error, losses may be amplified within a very short period of time. Authorization for AI financial agents must be limited authorization, not full authorization. At a minimum, it must clarify the following questions: who grants the authorization; which AI or system receives the authorization; which accounts and assets may be operated; what actions are permitted; what actions are prohibited; what the single-transaction limit is; what the daily or periodic limit is; how long the authorization remains valid; whether trading, transfers, redemptions, and rebalancing are permitted; whether external tools may be called; whether other AIs or sub-agents may be called; whether automatic iteration is permitted; under what conditions the agent must stop; under what conditions renewed confirmation is required; and who bears responsibility if something goes wrong. The most important point is that AI’s execution authority must not expand by itself. AI cannot automatically enlarge the asset scope, raise transaction limits, connect to new platforms, change risk levels, or modify authorization boundaries simply because it discovers a “better solution.” Authorization must also include a revocation mechanism. Especially in high-speed trading, automatic clearing, stablecoin redemption, and asset rebalancing scenarios, ordinary revocation may be too slow. Therefore, high-risk AI financial agents should have emergency-stop or circuit-breaker mechanisms: once unauthorized action, abnormal trading, risk-limit breaches, data conflicts, or revocation instructions from the authorizing party are triggered, the system should immediately suspend the relevant operation rather than wait for after-the-fact accountability. In one sentence: AI may execute automatically, but it cannot expand its own authority automatically; AI may execute quickly, but it must be capable of being stopped in time.

IV. Execution Authorization Is Not Iteration Authorization

In the question of AI agency, it is also necessary to distinguish between execution authorization and iteration authorization. When a user authorizes AI to execute a financial task, this does not mean that the user authorizes AI to modify itself. For example, if a user authorizes AI to manage a low-risk asset position, that only constitutes execution authorization. It should not automatically include model iteration, strategy iteration, code iteration, interface iteration, permission iteration, or the use of sub-agents. AI’s automatic iteration changes the execution system itself, and should therefore be treated as a higher-risk action. Strategy iteration may change the direction of investment. Code iteration may change the execution logic. Model iteration may change the method of judgment. Interface iteration may connect to new platforms. Permission iteration may expand the scope of operations. Sub-agent invocation may introduce a new chain of responsibility. None of these changes should occur silently. Therefore, AI iteration must leave a record. Major iteration must also be reconfirmed, or at least be verifiable. Minor iterations may be recorded internally; medium-level iterations should be auditable; major iterations must form version facts, and reauthorization may be required. Version facts should at least include the old version, the new version, the content of the change, the triggering reason, the risk impact, the explanation of authorization inheritance, the effective time, the rollback mechanism, and the responsible subject. Sub-agent issues should also follow a basic principle. If the primary agent is allowed to call a sub-agent, it must do so within the original authorization scope and record the result of that invocation. In principle, the authorizing party or the operator of the primary agent cannot sever the chain of responsibility by claiming that the action was completed by a sub-agent. If the sub-agent provider concealed risks, had system defects, acted with knowing fault, or violated interface agreements, it should also bear corresponding responsibility. In other words, sub-agents may divide labor, but the chain of responsibility cannot be broken. In short: execution can be automated, but iteration must be versioned; strategy can be optimized, but boundaries cannot change without leaving a trace; AI can be upgraded, but the upgraded AI cannot automatically inherit all prior authorization.

V. Verification Rises from an Audit Tool to a Risk-Control Tool

In traditional finance, verification is often understood as after-the-fact auditing. After an event occurs, auditors, regulators, courts, or clients examine accounts, records, and responsibilities. But AI financial agents change this. AI is too fast and too automated. If verification occurs only afterward, risk may already have been amplified. Therefore, in AI financial agency, verification should not merely be an audit tool; it should rise to become a risk-control tool. Risk control should no longer mean only setting rules or conducting after-the-fact inspections. It should become authorization verification, execution verification, response verification, exception verification, iteration verification, and responsibility verification. Before every high-risk financial operation by AI, the system should first verify whether authorization exists, whether it is valid, whether it has expired, whether it has been revoked, whether limits have been exceeded, and whether risk boundaries have been triggered. During execution, the system should monitor in real time whether the AI exceeds its authority, connects to unauthorized platforms, changes strategies, calls sub-agents, or encounters abnormalities. After the operation is complete, a verifiable receipt should be generated. This is consistent with the requirements in verifiable finance for subject facts, authorization facts, transaction facts, delivery facts, responsibility facts, and accounting facts. With the emergence of AI agents, agent facts, version facts, and exception facts must also be added. Financial facts should not only be auditable after the fact; they should become risk control conditions during execution. This means that the core risk control for AI financial agents is not to watch every line of AI code, but to ensure that AI cannot cross the verification structure. Code can be complex, but authorization must be clear; strategy can change, but boundaries must be verifiable; execution can be automatic, but responsibility must not disappear.

VI. AI Agents Must Have Real-Time Reporting Mechanisms

When human agents execute tasks, many processes do not leave complete traces. If a person helps you book a ticket, that person typically does not record every webpage viewed, every flight compared, or every option excluded. Human execution is often judged mainly by results. AI is different. Machines naturally can leave traces, and they naturally can generate reports. Precisely for that reason, an AI financial agent cannot merely deliver a result; it must report in real time at critical points. The reports here are not ordinary chat replies. They are verifiable receipts at the operational level. They should include at least several categories: authorization-check reports, explaining whether the current authorization is valid and within limit and duration; pre-execution reports, explaining what operation is about to be executed and which accounts, amounts, counterparties, and risks are involved; post-execution reports, explaining what was actually executed, what the result was, and what transaction or operation record exists; exception reports, explaining whether unauthorized action, failure, data conflict, risk-limit breach, or system abnormality occurred; iteration reports, explaining whether major changes occurred in the model, strategy, interface, code, or sub-agent structure; and responsibility reports, explaining which authorizing subject, service subject, or responsible subject should bear the consequences of the operation. AI may operate automatically, but it cannot operate silently. This is especially true in finance: silent execution is itself a risk. High-risk operations must be reported; medium risk operations should require confirmation; abnormal operations should be suspended; and major changes should require renewed authorization. Therefore, the operating mechanism of an AI financial agent should not be a simple sequence of “authorization — execution — result.” It should be: authorization — execution — report — reauthorization — record — verification — accountability.

VII. The Process Need Not Be Fully Public, but Key Facts Must Be Verifiable

Whether the execution process of an AI agent should be fully recorded and fully disclosed is another important question. If all processes are required to be fully stored and fully disclosed, the system will become too complex and too costly, and may expose trade secrets, customer privacy, and security vulnerabilities. Therefore, the proper principle is not full disclosure, but layered recording and layered disclosure. Raw execution details may belong to internal controls; key states must be visible to users; complete materials should be auditable by auditors and regulators; major incidents should be disclosed to the market; and key records should generate proofs to prevent after-the fact tampering. Normal low-risk operations may leave summary records. High-risk financial operations should leave complete records. Abnormal operations and major iterations must leave full records. Key hash proofs may be preserved over the long term. Raw data may be retained for defined periods. Verifiable finance does not mean exposing everything to everyone. It means ensuring that key facts cannot be hidden, tampered with, or rewritten after the fact. For AI financial agents, what truly needs to be preserved is not every step of what AI “thought,” but the key facts sufficient to answer the following questions: who authorized the action; what AI did; whether it exceeded its authority; whether it reported; whether iteration occurred; whether an exception was triggered; and to whom responsibility belongs.

VIII. The Advantage of AI Agency Is Not One-Time Authorization, but Interactive Authorization

The authorization model of traditional apps is usually one-time permission. The user agrees to terms, enables permissions, or enters a password, and the system then runs according to a preset program. The process is difficult to converse with, and it is difficult to adjust boundaries in real time. AI agents are different. AI can interact with the authorizing party during execution. It can report, request instructions, seek confirmation, explain, pause, and adjust execution boundaries according to new instructions from the authorizing party. This means that AI financial agents do not need to rely on one-time full authorization. On the contrary, they are better suited to continuous interactive authorization. Low-risk operations can be completed automatically and reported afterward. Medium-risk operations can be reported first and then await confirmation. High-risk operations must require renewed authorization. Actions beyond the original authorization must be suspended and request additional authorization. When market conditions, account status, prices, or risk conditions change, AI should proactively report. This is exactly where AI agents differ from traditional apps. Traditional apps are like mechanical buttons; AI agents are more like communicative execution systems. Since AI agents can communicate, they should not be given unbounded authorization. Instead, they should continuously accept boundary management during execution.

IX. The Minimum Viable Loop

This institutional design cannot be made overly complex at the outset, or it will be difficult to implement. The rules for AI financial agents should begin with a minimum viable loop. First, the AI agent must have an identity. Second, high-risk operations must have explicit authorization. Third, authorization must define scope, limits, duration, and risk boundaries. Fourth, authorization must be checked before execution. Fifth, a report must be generated after execution. Sixth, exceptions must cause suspension. Seventh, major iterations must leave records. Eighth, key records must be verifiable. Ninth, responsibility must return to a human or legal person. This is already sufficient to form the first-stage framework for verifiable financial agency. It does not require every process to be public. It does not require every line of code to be readable. It does not require humans to understand every operation of AI line by line. It only requires a minimum standard: high-risk financial behavior by AI cannot occur without authorization, without reporting, without records, without verification, or without attribution of responsibility.

X. Conclusion: The Stronger AI Becomes, the Less Vague Authorization Can Be

Financial risk in the AI era is not only that AI may be insufficiently capable. It may also be that AI becomes too capable while authorization, verification, and responsibility remain insufficient. The closer AI comes to human agency, the less it can continue to use the one time authorization model of traditional apps. The more automatically AI can execute, the more it requires limited authorization, real-time reporting, iteration records, and verifiable risk control. Humans can be fully authorized because humans can bear consequences. AI cannot be fully authorized because AI currently does not bear ultimate responsibility and does not enjoy ultimate interests. What AI receives is execution authority, not the status of a responsible subject. Therefore, the basic principle for AI financial agents should be: AI may be granted execution authority, but it cannot be granted unbounded execution authority. The key question for future AI finance is not whether AI can execute, but under what authorization it executes; not whether AI can optimize strategies, but whether it can expand its own authority automatically; not whether AI can generate results, but whether the authorization, process, exception, iteration, and responsibility behind those results can be verified. Here, verifiable finance is not merely an audit tool. It is the risk-control foundation for AI financial agents. As the previous article argued, AI cannot replace the external facts on which it depends; at the financial execution layer, AI also cannot be separated from limited authorization and real-time verification. The stronger AI becomes, the clearer its boundaries must be. The more automatic execution becomes, the more real-time reporting is required. The more intelligent agency becomes, the less it can be separated from verifiable authorization.

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