Before the agent takes any action, it will first send an inquiry: Signaling Technology has installed an intent confirmation node for the agent.

The year 2026 is known as the first year of the Agent era. From writing emails, booking flights, querying data to signing contracts, intelligent agents are rapidly being adopted across thousands of industries. Industry competition currently focuses on three core areas: model parameters, intent recognition accuracy, and execution link automation. As the accuracy rate rises from 80% to 90% and then to 95%, all players are competing for fewer errors and fewer mis-executions.

Yet a critical question is rarely seriously examined: are the results directly delivered by intelligent agents legally valid?

First Hallucination Lies in Comprehension, Not Output

When discussing large model hallucinations today, people usually focus on how the model reasons internally and why it outputs a certain result. But if you break down the process independently, there are at least three layers of hallucination: the first layer is how the model understands user input, the second is how the model reasons internally, and the third is why the model gives a specific output.

The industry devotes massive energy to the second and third layers, to make reasoning more stable and output more accurate. But the first layer of hallucination is most easily overlooked: the system directly takes the model's comprehension of user input as the user's actual intention. When a user says "I feel a bit cold", the model interprets it as "the user wants to buy clothes", and the system executes the instruction directly. The model may get the comprehension right or wrong. The problem is not about right or wrong, but that this comprehension has never been confirmed by the user, yet the system treats it as a fact agreed by the user. What we have broken through is exactly this black box problem in this step.

The second and third layers of hallucination can be mitigated by making the model more powerful. The first layer cannot be solved only by improving model performance, because no matter how intelligent the model is, it cannot replace the user to say: "Yes, this is exactly what I mean."

Guessing Right Is Probability, Consent Makes It Legally Valid

Legally, for an act to be binding on the user, the premise is that both parties have reached mutual assent. Mutual assent essentially means that both parties have reached an agreement on a specific matter, which is the foundation for subsequent execution, as reflected in the clause "agreed by both parties" in contracts.

When you go to a bank and say "I want to withdraw money", the teller will ask "How much would you like to withdraw?", and you reply "500". This question and answer is the confirmation of mutual assent. The teller cannot guess a number on their own, give you the money directly, and then say "I guess this is what you mean". A guess does not count as your consent.

In the architecture of mainstream intelligent agents today, this exact confirmation node is missing between intent recognition and direct delivery. The model outputs the most probable interpretation, and the system takes it as the user's intention to execute and deliver directly. The problem does not lie in whether the guess is accurate, but in the act of guessing itself. Guessing right is a probabilistic success; only explicit consent constitutes legal validity.

In traditional businesses, this node is borne by humans. When a client says "Help me check this", the manager will follow up and ask "Would you like to check the balance or make a transfer?". This follow-up is not designed by the system, but an instinct of human communication. In the era of intelligent agents, this instinct has disappeared, and has not been replaced by an institutional mechanism.

Audit logs answer what the system has done, while pre-execution confirmation answers what the user has consented to. The former is a post-event record, while the latter is a pre-event fact.

Intent Confirmation: Turning Guesses Into A Verifiable Starting Point

What Signaling Technology builds is not another intent recognition model, but a set of prepositive intent confirmation architecture. Its difference from existing solutions can be summed up in one sentence: existing solutions make guesses more accurate, while intent confirmation realizes the unique determination of user input and meets auditability requirements.

The core logic consists of three steps:

First, fix the intent before execution. Before execution and delivery, the system fixes the user intent it understands as a verifiable record.

Second, trigger confirmation unconditionally. Confirmation is a necessary precondition for execution and delivery. No matter how confident the model is, it does not constitute mutual assent.

Third, calibrate during execution. Once any deviation from the benchmark occurs during execution, the system will actively pause and re-calibrate.

This architecture not only solves the problem of what to do when the guess is wrong, but also answers the core question: on what legal basis is the delivered result valid? It rebuilds the legal validity foundation of intelligent agents into the intelligent agent business.

Not Only for the Financial Sector, All High Compliance Scenarios Require This

Mutual assent is not an industry constraint only for financial intelligent agents, but a universal legal premise for all intelligent agent interactions. Intelligent customer service is the most intuitive example.

When a user says "I want to make a complaint", if the intelligent agent guesses it as "I want to make an inquiry" and gives a section of inquiry guidance, this is not a response to the complaint, but an answer to a completely different question. The reply is sent out, but legally, it does not respond to the actual question the user raised. If customer information is accessed in this process, it may also involve legal requirements of personal information protection regarding awareness, consent, and compliance.

The same logic applies to the finance, healthcare, education, legal, and government service sectors, leading to the same conclusion: as long as the output of an intelligent agent needs to produce legal or regulatory effects, the user input must first be confirmed as a unique, auditable and reproducible intent, so that mutual assent can have a valid foundation.

The Most Prominent Pain Point in the Financial Sector: When a User Says "That's Not What I Meant", Can the Institution Produce Valid Evidence?

Finance is one of the sectors where intelligent agents are deployed the fastest and have the densest legal constraints.

Today, when a user tells a financial institution "That's not what I meant", almost no institution can produce evidence to prove what the user actually intended at that time. What institutions can provide are transaction logs, operation records, and model versions, which are evidence of what the system did, not evidence of what the user consented to.

Regulatory requirements exactly target this missing part. The document Jin Fa [2026] No. 8 requires that the responsibilities of the entire business process be clear, implementable, and traceable. The regulatory logic is very clear: businesses must be handled with explicit authorization from users, and must not exceed the defined boundaries. However, under the current architecture, it is impossible to provide clear authorization for business handling, because guessing does not constitute mutual assent, which means no valid business authorization exists, and no clear business boundary can be generated.

There is a structural gap between the verification obligation and the evidence presentation capability. What prepositive intent confirmation aims to solve is exactly this gap: when the regulator asks "How do you prove that the user authorized this operation", the institution can present an intent record that is confirmed, fixed before execution, and cannot be unilaterally explained afterwards. This is not an evidence chain for post-event remedy, but a pre-existing fact of mutual assent.

It Is Not Hitting the Brakes, But Installing a Legal Compliance Foundation

Signaling Technology (Hangzhou) Co., Ltd. is an innovative technology enterprise focusing on trusted AI decision-making infrastructure, committed to providing underlying middleware products for auditable intent recognition and multi-source data conflict adjudication for high-compliance industries including finance, government services, and healthcare.

The company's core team has cross-domain composite backgrounds, with in-depth understanding of AI technology and financial business. The company has taken a cutting-edge layout in trusted AI, and has completed a full closed loop from theoretical innovation to patent layout and product prototype, forming a complete set of trusted AI patents. It is currently promoting related standards to become global standards.

The target customers of Signaling Technology are all artificial intelligence enterprises that want to enter the EU market, for whom it provides compliance solutions for intent recognition nodes to meet the regulatory requirements of the EU Artificial Intelligence Act and DORA (Digital Operational Resilience Act). Meanwhile, this solution can assist compliance with EU regulatory frameworks including the General Data Protection Regulation (GDPR), Markets in Financial Instruments Directive II (MiFID II), and Unfair Commercial Practices Directive (2005/29/EC).

What Signaling Technology does is not to make intelligent agents do fewer things, but to ensure that every thing the intelligent agent does has a confirmable, traceable and provable starting point legally.

Confirm first, then execute, and deliver afterwards. This is not hitting the brakes on intelligent agents, but installing a legal compliance base for them.