---
title: "Retailers Must Balance Bot Blocking with AI Agent Access"
slug: "retailers-must-balance-bot-blocking-with-ai-agent-access"
date: "2026-10-30"
category: "Business & Society"
author: "Helena Ross"
correspondent: "Helena Ross"
correspondentRole: "Strategic Think-Tank Curator"
readingTimeMinutes: 2
tags: ["News"]
originalSourceUrl: "https://www.forrester.com/blogs/dont-block-all-the-bots-they-might-be-shopping-for-your-customers/"
canonicalUrl: "https://aurav.ae/retailers-must-balance-bot-blocking-with-ai-agent-access"
publisher: "Aura-V Intelligence (aurav.ae)"
---

# Retailers Must Balance Bot Blocking with AI Agent Access

> Retailers face new complexity in distinguishing harmful bots from AI agents that drive customer purchases.

## Key Takeaways (TL;DR)

- Over-blocking bots can cut off emerging AI-agent shopping journeys.
- Allowing all bots invites fraud and scraping risks.
- Vendors should provide transparent classification and support for AI-agent traffic.

Retailers have spent years improving bot detection, but the rise of AI-driven shopping agents means that some automated traffic is desirable. Blocking too aggressively risks shutting off emerging AI-agent-led customer journeys, while allowing too freely invites familiar fraud and scraping risks.

**Who AI agents are**
The article notes three categories of automated visitors that retailers already want to serve:

- An **agent** scraping product data for a marketplace, such as Amazon gathering data for its “Buy for Me” catalog.
- An **answer engine** like ChatGPT or Google Gemini accessing product content to surface items in synthesized answers.
- A **consumer-owned AI shopping agent** (e.g., Instinct or Muse) acting as a digital delegate in product discovery or purchase.

**Survey of bot-and-agent trust software users**
In Forrester’s Buyer’s Guide: Bot And Agent Trust Management Software, 2026, the research team interviewed 22 reference customers. Of those, 17 used bot-and-agent trust management software for account fraud, 14 for e-commerce fraud, and 14 for web and LLM scraping. Thirteen had started or were about to start applying this software to AI-agent trust.

**What retail technology leaders should do now**

- Stop treating every bot as the same problem. Define policies for malicious automation, known partners, answer engines, commerce agents, and customer-authorized shopping agents, and assign clear business owners for each category.
- Demand visibility and explainability from vendors. Require contextual information behind classifications and decisions, not just an allow/block outcome, and ensure teams can access sufficient signal to investigate incidents.
- Test for false positives in commerce-focused scenarios. Include legitimate AI-agent use cases in proofs of concept and evaluate detection strength, data quality, support responsiveness, and model-tuning capability.
- Bring commerce and security leaders to the same table. Commerce teams must decide which external agents to serve; security teams must determine how that access can be trusted and governed.
- Evaluate the vendor, not just its detection engine. Assess threat research, tuning ability, support, pricing model, and service options.

Our read: The data shows that a majority of surveyed retailers already use trust-management tools for traditional fraud vectors, yet only a minority have begun to extend those controls to AI-agent traffic. This gap suggests an emerging risk surface that vendors and retailers must address through granular policy frameworks and transparent, explainable tooling.

## Aura-V Editorial Verdict

<NONE>

---

*Original Think-Tank Source: [Don’t Block All The Bots. They Might Be Shopping For Your Customers.](https://www.forrester.com/blogs/dont-block-all-the-bots-they-might-be-shopping-for-your-customers/)*