Ruishu Information Warns Machine Traffic Now Dominates Internet as AI Agents Surge

Ruishu Information has published the 2026 Automation Threat Report, revealing that non-human traffic now constitutes the majority of internet activity. According to the findings, bots accounted for approximately 68 percent of overall traffic between early 2025 and Q2 2026, with malicious bots representing 55 percent of that share. Human-driven access has dropped to roughly 22 percent, while AI Agent-generated traffic has risen rapidly from less than 1 percent to between 8 and 12 percent.

Requests originating from LLMs and AI Agents have surpassed 450 billion, reflecting more than 400 percent year-over-year growth. Agent browsers alone now represent 9.7 percent of AI Agent traffic and recorded more than an eightfold quarter-over-quarter increase, making them the fastest-growing segment. The report concludes that the internet has entered a new phase in which machines are no longer mere participants but primary users.

From Traditional Bots to Autonomous AI Agents

The report redefines non-human traffic into three categories: traditional bots, AI-enhanced bots, and AI Agents. Traditional bots typically follow pre-set rules for tasks such as scraping or credential stuffing. In contrast, AI Agents can understand objectives, plan actions, invoke tools, and adapt based on environmental feedback. To describe this evolution, Ruishu Information introduces an L1-L5 threat framework ranging from low-autonomy scripted attacks to multi-agent systems capable of independent planning and collaboration.

New Attack Surfaces and Industry Impact

The addition of AI Agents has expanded the list of tracked threat scenarios from nine to thirteen. Four new categories include LLM application attacks, agent supply-chain compromise, agent identity impersonation and hijacking, and autonomous AI-orchestrated attacks. Financial services recorded a 60 percent share of malicious bot traffic, while the internet sector led in AI Agent traffic at 15 percent. Manufacturing showed the fastest growth in automation-related threats.

Shifting from Detection to Trust Governance

Traditional defenses relying on IP addresses, user agents, or static fingerprints are becoming less effective against sophisticated agents that can operate inside legitimate browser environments. The report advocates moving toward a trust-governance model that evaluates identity, behavior, and intent. Under this approach, high-trust traffic receives normal access, medium-risk traffic faces rate limiting or additional verification, and low-trust traffic is subject to stricter controls or blocking.

Ruishu Information recommends five core measures: WAAP full-link defense, full-lifecycle data protection, dynamic environment detection, attack-chain correlation, and dedicated agent trust governance. These controls aim to reduce reliance on static signatures through dynamic obfuscation, tokenization, and fingerprinting techniques while enabling differentiated handling of compliant versus malicious machine traffic.