Detect agentic traffic on your website and enforce guardrails with client-side controls. Improve the purchasing experience for trusted agents while preventing content scraping, fake profiles, and other AI-bot driven fraud.
Agent MonitorAI brought us the Business to Agent (B2A) era. Brands are racing to win over millions of new website "shoppers". Defending against millions of AI website attackers will be left for security teams as an after-thought.
And the technical instruments to deal with this do not exist yet. Traditional bot detection checks "are you a human". Now teams need to check "are you acting on behalf of a human" - and validate if those actions are with good intention (to purchase) or bad intention (scrape content, find vulns., test credit cards).
We're a team of veteran security engineers. After specializing in the client-side space (monitoring what happens in the browser) for years, we're applying a unique detection engine to this "agentic trust" challenge.
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McKinsey predicts global revenue orchestrated from agentic commerce hit $3 to $5 trillion by 2030.
In 2025, both VISA and Mastercard launched infrastructure to accept agentic payments.
Private users will be invited to use our platform. Your feedback will shape what we ship.
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Good "consumer" agents need guidance on your website. For example, on a checkout page, how should an agent handle selecting upsells?
Proper optimization leads to more revenue. Poor optimization leads to angry calls to the bank saying "your website tricked my AI agent into buying more than I asked for".
8/10 times they failed to detect our malicious AI agents. And we were barely trying. In fact we intentionally tried to get caught and still slipped through most times.
This made it clear to us that "bot detection" tools are not ready for:
→ Pirates: Scraping premium content at scale (video streaming, music, art)
→ Payment Fraudsters: Credit card testing, chargeback abuse
→ Hackers: Brute force scanning for vulnerabilities, creating false accounts
AI agents reveal themselves in the browser, where traditional bot detection tools have weak visibility. Browser-layer (or client-side) monitoring keeps agents within safe boundaries.
With cside:Agents interface with APIs and MCPs, but many of them rely on a "browser" to complete tasks. Just like humans, the easier the experience is, the more they come to you.
With cside:Fraudulent agents simulate real buyers to abuse coupons, test stolen credit cards, and distort analytics.
AI agents scrape premium content to feed piracy networks or train other LLM models without permission
Agents automate refund arbitrage and seat-blocking attacks.
Autonomous agents attempt to submit deepfaked KYC info and micro transfer fraud.
Automated agents can scrape content from streaming or art marketplace platforms at scale. Content is republished or used to train LLM models without permission undermining the exclusive content revenue model.
LLM powered bots now reason around CAPTCHAs and queue systems, securing tickets faster than humans. Scalpers resell those tickets at a premium to genuine fans, damaging your consumer trust.
Synthetic agents generate real identities, creating fake accounts that poison analytics or abuse sign-up rewards. Agents can maintain their identity by responding to messages or interacting with your platform as if they were human.
AI agents test thousands of card numbers across domains using reasoning to avoid detection. Spacing requests, rotating proxies, and using human-like timing keeps them hidden from traditional fraud tools.
When an AI agent encounters a checkbox or button for an upsell, it needs clear guidance on how to proceed. You can define escalation rules that require human approval, preventing unintended cart changes that customers will dispute.
Set governance rules for what actions AI agents can perform. Allow safe actions such as browsing or cart additions, while switching agents into read-only (or no access) mode on sensitive pages.
Instead of blocking every bot that isn't Google, identify AI agents with commercial intent. Track where their actions fail and use that insight to improve agentic conversions for new revenue.
FAQ
Frequently Asked Questions
Some agents interact with APIs and MCPs. These are systems where agents access your site through code, sending questions to your server and receiving responses. Many agents will also interact with your website through a "browser" in the same way a human would. This is known as the client-side. The client-side includes visual interface elements along with code interaction. Client-side monitoring from tools like cside look at code execution and behavior in browser sessions, which reveal clues and grant control that API, MCP, and server-level security tools miss.
Agents reveal themselves through browser layer signals. Often times they show known IPs or signatures from major LLM platforms (ChatGPT, Anthropic, Amazon). Fraudulent agents may try to hide their identity but can be caught by looking at timing patterns, fingerprint mismatches, suspicious network requests, and behavior on your web pages. The easiest way to identify agents is through an AI bot detection solution like cside. This platform shows you a dashboard of known and unknown agents on your site and what they are doing.
If you auto block anything that looks automated, you'll also block legitimate agents. A better approach is to use a tool like cside that can block AI agents based on behavior. You can set rules that adapt according to where an agent is coming from, if their identity is known, and a perceived trust score from their behavior.
Yes. They already are. Tools like Amazon Buy For Me are processing purchases end to end for consumers. Mastercard and VISA both launched infrastructure in 2025 to accept agentic payments. While some consumers might be hesitant to allow agents full buying power, agents are also comparing prices, checking stock availability, doing research, and performing other tasks in the "buying journey".