Know Your Agent – How to Verify AI Agents at Scale

The rise of AI agents is reshaping the digital landscape, and fast. From personalized shopping assistants to automated travel planners, intelligent agents are beginning to represent a significant share of online activity. For instance, Adobe Analytics reported a staggering 1,800% increase in retail site traffic driven by generative AI chatbots during Black Friday 2024, compared to the previous year.

But this is just the beginning.

According to Gartner, by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.

AI agents will also have a significant role in digital transactions. In other words, it won’t be long until “bots buying from other bots” becomes a standard in the payments industry (Source: Forbes).

However, as these bots become more prevalent, businesses face a critical challenge: distinguishing between legitimate agentic AI interactions and malicious bot activities.

Standard security measures will simply not apply in this new landscape. A new "Know Your Agent" (KYA) process will be needed: a framework designed to authenticate and manage AI agents, ensuring they align with organizational goals and security protocols, guided by similar principles of existing Know Your Customer (KYC) and Know Your Business (KYB) procedures in regulated sectors.

Why AI agents will reshape digital traffic

The integration of AI agents into various sectors is set to redefine digital traffic patterns. These agents, able to perform tasks autonomously, are increasingly handling functions traditionally managed by humans. Their ability to operate continuously and efficiently makes them attractive assets for businesses aiming to enhance user experience and operational efficiency.

In the retail sector, AI agents are improving customer interactions. During the 2024 Black Friday event, the deployment of generative AI chatbots led to a significant uptick in online sales, with U.S. e-commerce reaching $10.8 billion, marking a 10.2% increase from the previous year. These AI-driven interactions not only facilitated transactions but also improved customer satisfaction by providing personalized assistance and swift responses.

The travel and hospitality industries are also experiencing transformative impacts from AI agents. These agents assist users in planning trips, booking accommodations, and managing itineraries, offering a seamless and efficient experience. The automation of these processes helps businesses cater to a broader audience while optimizing resource allocation.

However, the rise of AI agents also brings challenges. The potential for malicious bots to mimic legitimate agents poses security risks, including data breaches and fraudulent activities. Therefore, implementing robust verification frameworks like KYA becomes imperative to safeguard digital platforms while leveraging the benefits of AI agents.

Which sectors will be hit first?

Some industries are more exposed to this shift than others, particularly:

E-commerce
AI shopping assistants already help users browse, compare, and purchase products across multiple sites. Retailers that block AI agents will disappear from these shopping journeys.

Travel
AI agents will soon plan itineraries, compare flights, and book hotels. If platforms block all bots, they risk being excluded from these decisions entirely.

Real estate
AI agents can now aggregate listings, compare property values, and recommend homes or rentals based on user prompts. Agencies that block automated traffic may become invisible to these increasingly popular assistants.

In short, companies that treat all bots as bad actors risk leaving serious money on the table.

Key factors that define a legitimate AI agent

If the internet is going to be increasingly populated by autonomous agents, platforms need a clear set of standards to tell the good from the dangerous. For example, KYC evaluates human users through a combination of attributes and behaviors, while KYA will rely on specific signals that distinguish trustworthy AI agents from hostile automation.

Here are the foundational traits that define a well-behaved, permissioned AI agent:

Respects boundaries
Legitimate agents operate within constraints. They don’t attempt to bypass CAPTCHA mechanisms, click erratically through a site, or crawl areas explicitly restricted through robots.txt. Their navigation mirrors user intent, not adversarial probing.

Discloses identity
Rather than masking traffic through residential proxies or spoofed headers, verified agents identify themselves clearly through standard user-agent strings or API tokens. Transparency is not an afterthought; it’s expected behavior.

Adheres to traffic norms
High-volume, high-frequency scraping patterns are hallmarks of abuse, not service. In contrast, functional AI agents maintain predictable request intervals and avoid overwhelming endpoints. They behave like polite intermediaries, not opportunistic scrapers.

Has traceable origins
Knowing who built the agent and what it’s authorized to do matters. Whether it’s an open-source project or a commercial AI platform, traceability creates accountability. It also makes it easier for platforms to classify traffic without blocking valuable sessions.

Leaves behind structured activity logs
Unlike stealthy bots that erase traces or simulate human randomness, good agents generate consistent and auditable logs. These logs offer a basis for platform owners to understand what the agent requested and how it interacted.

Operates within observable limits
Legitimate AI agents function within measurable operational thresholds. They don’t initiate infinite loops of requests, flood endpoints, or mimic distributed denial-of-service patterns. Their activity can be modeled and predicted, which makes them easier to verify and control.

Accepts feedback or policy enforcement
Good agents were designed to listen. Whether through rate-limiting, header-based policy cues, or structured API responses, well-designed agents adjust their behavior when the platform pushes back. A refusal to adapt or comply is often a signal that the agent is built with adversarial intent.

What the KYA process might look like

KYA is a structure that needs to evolve as AI agents grow more capable and more common. While every platform’s needs will differ, the core components of a functioning Know Your Agent framework will likely share a few essential elements.

Agent registries and trusted identity schemas
Just as domain names are linked to verified owners, AI agents could be registered under a known identity, signed by developers or platforms that meet certain standards. This identity doesn’t need to reveal sensitive internal logic, but it should confirm authorship, provider, and version control. Whether through a formal registry or an industry-standard authentication header, legitimacy should be traceable.

Scope and permission mapping
Knowing what an agent is allowed to do (search, retrieve, book, summarize) is just as important as knowing who built it. A standard KYA implementation might track declared intent against actual behavior. If an agent requests read-only content but begins triggering transactional endpoints, that mismatch should be flagged immediately.

Real-time behavioral assessment
Static identity checks aren’t enough. A meaningful KYA system monitors how agents behave once granted access. Request volume, navigation logic, endpoint targeting, and timing patterns all help determine whether the agent is acting in alignment with its stated role.

Audit trails and post-session logs
Platforms should retain structured logs of AI agent interactions. This doesn’t require invasive monitoring, just consistent tracking of what was accessed, when, and how often. These records serve as both a security measure and a tool for improving AI-agent collaboration in commercial workflows.

Compliance and data integrity signals
Even AI agents can mishandle sensitive information or violate regional privacy rules. A robust KYA process might include metadata checks to ensure that data usage aligns with jurisdictional privacy laws, especially in sectors like healthcare, finance, and travel.

Preparing platforms for the next wave of digital interactions

As AI agents become a fixture of how users search, compare, and transact online, digital platforms must evolve to recognize and manage this new class of traffic. The main question is whether businesses are equipped to evaluate them correctly when they do. KYA offers a structured approach to assessing AI agents based on transparency, behavior, and source legitimacy. It helps platforms stay visible in AI-driven environments while filtering out high-risk automation. Building these verification layers now allows companies to remain discoverable, secure, and commercially relevant as digital interactions shift from human fingertips to machine interfaces.

FAQs

Can AI agents make purchases on behalf of users today?
Yes, several platforms are already experimenting with transactional agents that can complete bookings or purchases using saved credentials or APIs. These actions are typically restricted to closed environments but signal a shift toward fully autonomous transactions.

Are there any standardised KYA procedures businesses should follow?
As of now, Know Your Agent (KYA) procedures; analogous to Know Your Customer (KYC) or Know Your Business (KYB), are not yet widely standardized or formally established across industries. However, early frameworks and discussions are emerging, especially in organizations experimenting with AI agents and autonomous systems.

Are there existing standards for identifying AI agent traffic?
There is no universal framework yet, but initiatives are emerging—like standardized user-agent naming conventions and signed requests—that allow platforms to recognize known AI agents. Adoption is still limited and varies widely between providers.

How do AI agents differ from traditional crawlers or indexers?
Unlike traditional bots that perform linear tasks like indexing pages, AI agents interact dynamically, interpret intent, and may even modify behavior based on real-time responses. This makes them more adaptable—and more complex to monitor or restrict effectively.