When Vercel disclosed in April 2026 that an intrusion into its internal systems began with the compromise of a third-party AI agent platform connected to an employee's Google Workspace account, it crystallized a risk many security leaders had been tracking but few had operationalized. The attacker did not phish an engineer. They did not defeat MFA. They compromised an AI tool the employee had quietly connected months earlier, and rode its existing OAuth grants into the enterprise.

AI is already making decisions inside enterprises and most organizations are not prepared, with fragmented data, brittle integrations, and limited governance struggling to keep up. On June 11, Yenlo and WSO2 invite you to Oxygenate: Building the Agentic Enterprise, an exclusive half-day event for enterprise leaders exploring what AI means for business now and for leadership in the near future. Hear from Sanjiva Weerawarana, Frans Feldberg, and Dini Klaassen, alongside a panel of industry leaders, at Beeld & Geluid in Hilversum from 13:30 to 18:30. Reserve your seat now.

A supply chain compromise and fraudulent compliance certifications hit LiteLLM in the same week. Here's what engineering teams should evaluate next.

Why teams are reevaluating LiteLLM in 2026

Two things went wrong for LiteLLM in the last week of March 2026. Together, they've turned LiteLLM security into a front-page topic and shaken confidence in one of the most widely adopted AI gateway libraries in the Python ecosystem.

Enterprises are moving toward a new North Star: becoming an agentic enterprise. This is a multi-year shift in architecture, operations, and governance as autonomous AI agents become first-class actors in how work gets done. WSO2 provides the foundational technology fabric—agent tooling, APIs, integration, identity, and engineering foundations—to make that shift practical and safe, without lock-in. The practical question for most organizations is whether their digital infrastructure is ready for that shift.

Advances in large language models (LLMs) and their widespread accessibility have transformed both what software can do and how we build it. The use of LLMs has quickly evolved from simple single-turn interactions to AI agents that reason, use tools, manage state, and operate autonomously.