Agentic traffic is web and mobile activity generated by AI agents acting on behalf of a customer, from signed assistants to computer-use browsers that work inside the customer's own session. For banks, fintechs, and NBFCs, AI agent detection now sits alongside bot defense as a core fraud control, because AI agent fraud can look like ordinary customer activity.

AI agents form the foundation of agentic commerce, shopping, moving money, and managing accounts on behalf of customers. They also drive agentic commerce fraud. For banks, NBFCs, and fintechs, this shift creates a new class of digital visitors: software that acts with a customer's permission, sometimes on that customer's own device, and often without announcing itself. Traffic that was once divided into humans and bots, now has a third group; and the line between a helpful delegate and an abusive script is harder to see. Fraud leaders who can tell the two apart will protect revenue and keep legitimate customers moving. Those who cannot will either block their next wave of customers or wave through attackers dressed as assistants.

Why Does Agentic Traffic Change the Fraud Equation?

Most identity controls rest on the assumption that the person who passed verification is the person acting. Agents separate the actor from the accountable party. A customer authorizes an agent, the agent logs in, and the session looks like it is driven by the customer. However, it is the software that's actually making the decisions.

Adoption of AI agents is fast. Liminal research shows 86% of practitioners already observe AI agents acting on behalf of consumers in commercial settings, including 85% in financial services. Exposure to AI agent account takeover in banking is also following closely: 19% of account takeover attempts now involve agent-driven tactics, nearly matching credential stuffing at 20%.

Blocking every agent would turn away customers who chose to delegate. Allowing every agent would open the door to fraud. So, the workable path is to classify traffic at the session level with a response matched to the risk of each action.

What Are the Three Types of Automated Visitors?

A useful starting point is to separate agentic traffic into three groups, namely:

• Signed agents declare themselves. They attach cryptographic signatures to their requests, using approaches such as Web Bot Auth, a standard now moving through the Internet Engineering Task Force (IETF), the body that develops internet standards. Although a valid signature confirms who sent the request, it says little about what the agent intends to do once inside an account.

• Unsigned agents carry no declaration. Computer-use agents and AI browsers can operate through a customer's own browser or device, clicking and typing the way a person would. The majority of AI agents sit in this group, and a signature check alone never sees them.

• Automated abuse covers scripted bots, emulators, credential stuffing tools, mule farms, and loan-stacking rings. These visitors have every reason to look like legitimate agents or like ordinary customers.

Each group calls for a different response. So, detection has to answer three questions together: who is driving the session, has that driver been verified, and should it be trusted for this particular action?

How Can Fraud Teams Distinguish AI Agents from Bots and Automated Abuse?

No single signal can reliably separate a legitimate agent from abuse. Accurate classification combines several, as enumerated below with each one confirming or challenging the others:

• Cryptographic verification handles agents that choose to identify themselves. It is the fastest path to a confident answer where it applies.

• Device intelligence examines the environment behind the session: the browser, the hardware, the network, and how consistent they are with the account's history. A familiar customer device behaves differently from an automation framework or an emulator farm.

• Behavioral biometrics examine how the session unfolds: the rhythm of input, the path of navigation, and the timing between steps. People hesitate, correct themselves, and move unevenly. Software tends to act with a precision and consistency that stands out across a full journey.

• Session context ties it together. The same device, the same account, and the same action can mean very different things depending on what came before and what comes next.

Bureau's Agent Session Detection combines device intelligence and behavioral biometrics to identify unsigned and computer-use agents on top of signed ones. It returns labels with confidence scores instead of single verdicts, because a session's driver can change while it is still running.

What Is Mid-Session Takeover?

The hardest case for traditional defenses is the handoff. A customer logs in from a familiar device, passes authentication, and then an agent takes over the session, or the reverse. Every checkpoint at the front door was passed by a real person, yet the later actions came from software.

Session-level detection addresses this challenge by separating human segments from agent segments within one journey. It can flag a known human device that is suddenly being driven by an agent, and mark exactly where control changed hands. That precision matters most around sensitive actions such as adding a beneficiary, setting up a mandate, or changing contact details, where the identity of the driver carries the most risk.

How Do Banks Prove What a Customer Authorized?

Detection tells a bank what happened. Disputes, audits, and regulators ask for something more demanding: what did the customer actually authorize?

This is also where buyers feel the gap. 60% of practitioners cite compliance risk from unauthorized agentic access. Session segments that separate human steps from agent steps supply that record. They give dispute teams evidence of who initiated a payment, who consented to a change, and where an agent acted inside or outside its delegation.

One-time passcodes show the same issue. 72% of buyers say single-signal authentication cannot stop fraud and an agent with access to the customer's inbox or messages can read the code and enter it. Knowing whether a person or an agent completed the OTP step turns consent assurance into a control that holds up when the inbox itself is delegated.

How to Detect AI Agents: A Practical Playbook for Fraud and Risk Leaders

Fraud and risk leaders can follow the strategic approach described below for agent detection:

1. Classify at the session level. Label every session as human, signed agent, unsigned agent, or automated abuse, and record confidence along with the label.

2. Tier responses by action risk. Let low-risk browsing proceed, apply step-up authentication to high-value agent transactions, and block patterns that match mule farms, loan stacking, or account takeover.

3. Preserve session evidence. Keep the human-versus-agent timeline for each sensitive event so dispute and compliance teams can reconstruct who acted and when.

4. Watch for a takeover. Monitor for changes in driver within a session, especially on trusted devices and around beneficiary, mandate, and credential changes.

5. Bring the right owners together. In most organizations, ownership of the response to an agent-driven incident is unclear. Name an owner across security, fraud, and compliance, and fit agent controls inside the identity and risk platforms already in place.

Conclusion: Building Trust in Agentic Traffic

Agentic traffic will keep growing, and a large share of it will come from real customers who simply prefer to delegate. The institutions that succeed will welcome that traffic while keeping evidence of who was in control at every sensitive step. Signatures, device intelligence, behavioral signals, and session context each contribute a piece of that picture, and together they let a team say yes to legitimate agents and no to abuse with confidence.

In short, Bureau's Agent Session Detection uses device intelligence and behavioral biometrics to tell people, agents, and automated abuse apart, flags mid-session handoffs, and returns confidence-based labels that drive step-up decisions and dispute evidence.

To see Bureau's Agent Session Detection live in action inside BFSI journeys, talk to an expert at Bureau.

Frequently Asked Questions

How Can Banks Tell a Legitimate AI Agent from a Malicious Bot?

Banks combine cryptographic verification for agents that sign their requests with device intelligence, behavioral biometrics, and session context for those that do not. Confidence-based labels then drive the response: allow low-risk activity, apply step-up authentication to high-value agent transactions, and block patterns linked to mule farms, loan stacking, or account takeover.

What Is Agentic Traffic?

Agentic traffic is web and mobile activity generated by AI agents acting on behalf of a person or business. It includes signed agents that declare their identity, computer-use agents that operate inside a customer's own browser or device, and AI browsers. Fraud teams need to separate it from human sessions and from automated abuse.

What Is the Difference Between a Signed and an Unsigned AI Agent?

A signed agent attaches a cryptographic signature to its requests, such as Web Bot Auth, so a site can verify who sent them. An unsigned agent declares nothing and can run through a customer's own browser. Signatures verify the first group, while device intelligence and behavioral biometrics identify the second.

What Is Mid-Session Takeover?

Mid-session takeover occurs when control of a logged-in session changes hands, such as an agent starting to drive a session a customer began on a familiar device. Session-level detection separates human segments from agent segments and flags the handoff, which matters most around actions like adding a beneficiary or setting up a mandate.

Why Is OTP Step-Up Not Enough Against AI Agents?

An agent with access to a customer's inbox or messages can read a one-time passcode and enter it, so an OTP alone cannot confirm that a person is present. Knowing whether a person or an agent completed the OTP step restores the value of the check and supports consent assurance.

What Evidence Do Banks Need to Prove What a Customer Authorized?

Banks need a record showing who initiated each sensitive action and whether a person or an agent performed it. Human-versus-agent session segments provide that timeline, giving dispute and compliance teams evidence of consent and of where an agent acted inside or outside its delegation.