TL;DR: Autonomous consumer sessions are now completing logins, forms and purchases, with Arkose Labs finding that websites must govern them as legitimate activity rather than treat every machine-driven session as abuse. Edge identity controls now need policy that separates allowed agentic traffic from hostile automation without breaking conversion.
Editorial analysis by NHI Mgmt Group, based on content published by Arkose Labs: “Population Two Arrives”.
Key questions
Q: Why do traditional customer IAM controls break down when AI agents act on behalf of users?
A: Traditional customer IAM often assumes a person is clicking through a session with clear intent and visible actions.
Q: What are the signs that agentic traffic is being misclassified?
A: Watch for abandoned but otherwise valid sessions, unexpected friction at login or checkout, and large differences between activity at search, authentication and payment stages.
Q: How should security teams detect AI agent traffic without blocking legitimate customers?
A: Security teams should use layered detection that combines browser, network, and behavioural signals rather than relying on a single indicator.
Practitioner guidance
- Define a consumer-agent traffic class Create a distinct handling path for browser-based autonomous sessions so they are not merged into generic bot or human categories.
- Instrument decision points by funnel stage Measure what happens when an agent searches, authenticates, creates an account and checks out, because risk changes by step.
- Verify session behaviour, not just declared identity Use runtime signals, transaction context and challenge outcomes to decide whether a session should proceed.
Bottom line: Consumer AI agents are forcing websites to govern legitimate autonomous sessions, not just detect hostile automation.
Glossary terms: Customer AI agent, Population-based Trust, Agentic Traffic, Runtime Trust
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Consumer agent traffic is not just another bot category: it is a new identity class that inherits legitimate user authority while acting with machine speed and timing. That means bot management, fraud controls and session trust can no longer share one flat policy model. The practitioner conclusion is that agentic traffic needs its own classification and decision path.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should teams do when an autonomous session reaches payment or account setup?
A: Apply stricter policy at the point where irreversible or fraud-prone actions begin, rather than relying on the same treatment used for browsing or form filling. If the session is authorised but machine-driven, the decision should hinge on transaction risk, customer intent and challenge results, not on the fact that the account exists.
👉 Read our full editorial: Consumer AI agents are creating a new identity decision layer