Zscaler unveiled a zero trust platform to secure autonomous AI agents’ data access, communications and device activity. It adds an AI Broker for agent-to-agent and data calls, endpoint AI threat detection, an AI Access Graph for mapping identities and data flows, and expanded AI Protect controls. This aims to give each AI agent its own identity, permissions and real-time monitoring.

The article shows how real-world agentic AI deployments can blow through budgets because multi-step workflows use 5–30× more tokens per task than simple chatbots. It breaks down four hidden cost layers—LLM inference with re-sent context, context rot, tool orchestration, and infrastructure—and offers strategies to curb runaway spending before your production bill arrives.

Agentic AI workflows trigger multiple model calls per task, driving token use 5–30× higher than simple chatbots and blowing through pilot budgets in production. Hidden expenses like re-sent context, context rot, tool orchestration, state management, and retries further inflate operating costs. The article breaks down these layers and offers strategies to control spend before your invoice arrives.