Every serious conversation about AI security eventually arrives at the same realization: the traffic hitting your applications no longer comes from only humans and the bots you already know how to classify. A third population has arrived: AI agents that are acting on behalf of legitimate customers, researching products, comparing rates, assembling carts, and preparing to transact. This is not a fringe pattern confined to a few tech-forward verticals. It is happening across every industry, and no organization is immune.
For some, the stakes are structurally larger. For retailers, agents introduce a real risk of disintermediation. When a customer delegates the research and the purchase to an agent, the brand experience you spent years building can collapse into a single API call. You become the sales endpoint: the emotionless destination rather than the conversion journey. The same dynamic is emerging in financial services, travel, insurance, and anywhere comparison and transaction can be delegated. The agent, not the customer, becomes the entity you actually interact with, and if you can’t recognize it, you can’t shape the interaction, let alone protect it.
The critical balance you can’t afford to get wrong
This creates a balance that has to be navigated deliberately. Add friction to stop agents and you risk taxing your own revenue, degrading or blocking the exact journeys your best prospects are trying to complete. Add no controls and you expose yourself to abuse, fraud, and unauthorized automation operating at machine speed. Blindly blocking all non-human traffic at the edge is clearly the wrong outcome, but so is waving all of it through. The right posture sits in between, and you can only find it if you can see what’s actually happening, and from whom.
Visibility is, as always, the first step
This is why visibility is the first objective. Organizations need to know who these agents are, what they are trying to accomplish, who they represent, and whether their behavior is beneficial, unknown, or potentially malicious. Today most cannot answer even the basic questions:
- Which AI agents are accessing our applications? Without identity, every downstream decision is a guess. You cannot apply policy to a population you cannot name, and you cannot distinguish a customer’s assistant from an adversary’s tooling.
- How much of our traffic is agent-driven? This is the sizing question. It tells you whether agents are a rounding error or a material and growing share of demand, and it determines how much this shift should influence your architecture and roadmap.
- Which customer journeys are being explored by agents? Knowing that agents concentrate on pricing, availability, or checkout tells you where value and risk are converging, and where the experience is worth optimizing rather than defending.
- Which agents are assisting prospective customers versus attempting abuse or fraud? This is the intent question, and it is the one that converts raw visibility into policy. The same behavior can be a high-value prospect or an attack depending on who is behind it and what they intend.
Once you can answer these, you can make informed policy decisions rather than blanket ones. In many cases the right decision is to allow, and even optimize for, legitimate customer agents, while applying tighter controls only when an agent becomes abusive, unauthorized, or inconsistent with the customer’s intent. That is governance, not a gate.
Why this isn’t just bot management
This is also where the distinction from traditional bot management becomes important. Bot solutions were built to answer a binary question at the edge: is this automated, and if so, keep it out. That model assumes automation is unwanted by default, and for scrapers, credential stuffers, and scalpers it works well and remains an essential part of the architecture. But customer agents break the assumption the entire category was built on. They are automated and often legitimate and can even appear and behave undetectably as humans. They act on behalf of a real person with real intent to transact. They arrive through sanctioned channels and may or may not identify themselves. Blocking them because they are automated is not a defensive win, it is lost revenue.
The solution is understanding and governance across the full ecosystem of customer and third-party agents interacting with your business: who they are, who they represent, what they are trying to do, and whether that intent aligns with your customer’s. That is a different problem than the one bot management was designed to solve, and it requires a different layer of intelligence.
Understanding AI
Understanding AI is that layer, and it is an entirely new intelligence category for security. We’ve built the solution from the ground up with AI Intelligence. 30 minutes and we’ll show you how it works.
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