Companies Deploy Agentic AI Through Data Structuring and Security Measures

Companies are increasingly moving beyond simple AI chatbots to employ agentic AI, which can perform tasks autonomously. This evolving technology enables agents to search databases, send emails, and analyze company data to automate workflows. In the first half of 2026, European agentic AI startups alone raised €7.9 billion in funding, surpassing last year's total.

Successful integration of agentic AI hinges on building a sophisticated "perception layer." This layer helps AI interpret unstructured data from various sources, including emails, Slack messages, and documents. Companies like Rezonant assist in creating tailored "context graphs" that organize scattered information according to business categories such as sales or finance. This enables agents to efficiently pinpoint the data needed for decision-making.

Many organizations delay AI adoption by waiting for their entire technology stack to be modernized. Platforms like N8n offer more flexible solutions, allowing data to be pulled from legacy systems, analyzed by separate AI models, and moved into newer systems without a full infrastructure overhaul. This approach helps avoid what Lindsay Keim, VP of customer success at N8n, describes as "data cleaning purgatory."

When agentic AI requires external information, such as from the internet, its unpredictability presents a challenge. Rotem Weiss, founder and CEO of agentic search company Tavily, explains the need for an "abstraction layer" to convert messy web content into consistent context. To ensure security and governance, companies are prioritizing "trust boundaries" that separate internal data from the public internet, alongside human-in-the-loop safeguards. The error handling and permissions of agents must be carefully defined to prevent them from overstepping boundaries, while still allowing them to learn task-specific routines.