Building the AI Layer Cake: How Calix Ensures Security, Accuracy, and Trust

There is a lot of hype around AI these days. But did you know that most enterprise AI projects actually fail in the pilot stage? According to MIT, 95 percent of today’s AI pilots collapse. Why? 
 

AI projects fail today for many reasons, but one of the most common is that it isn’t built on a solid foundation.
 

Think of agentic AI as a layer cake. If one layer is weak, the whole system will fail. And if the whole system fails, trust in AI is ultimately broken. True trust in AI requires well thought out layers—and every layer has a distinct role to play:

• Data Layer: Clean, accurate information. AI needs this to understand what’s going on and make smart decisions. This is the foundation of the whole platform. A well-defined data layer ensures reliable sources and quality. Without it, the AI systems will falter, producing unreliable outcomes—and they cannot learn. In other words, “garbage in, garbage out.” The data ingestion pipeline is where we want to fix any data quality issues and filter out anything that is trying to pollute the model.
  
  
• Knowledge Layer: Provides context to the data to drive action. A generic generative model trained on the open internet often hallucinates, because it doesn’t have the right context or information, so it’s ultimately guessing. That’s why Calix leverages a Retrieval Augmented Generation (RAG) architecture and in conjunction with Large Language Models (LLMs) as key components of the knowledge layer. This layer acts as a cognitive framework, establishing relationships and applying rules and specific meaning to the underlying raw data. This ensures AI agents have the right context alongside clean data, so answers are reliable and relevant. We also leverage Model Context Protocol (MCP) to create a federated knowledge layer with other business data sources such as martech, trouble ticket, or CRM systems.
  
  
• Orchestration Layer: Ensures accurate performance at scale. Even with good data and context, the system can fail if it’s not orchestrated correctly. Orchestration ensures that processes and workflows are managed, co-ordinated, and executed in the right order, at scale, so outcomes stay accurate and predictable. Consider an orchestra conductor: While they do not play an instrument, they ensure every musician plays the right part—at the right time.
  
  
• Trust Layer: Governance, access, and stewardship. At Calix, we define a trust layer not just as access control, but as stewardship. This is about governance. Are we allowing the right agent to act on behalf of the right person, and are we doing the right things with your data once access is granted?
  
  
• Security: Protects data across every layer. Security is about keeping customer and subscriber data safe and protected from exposure, misuse, or unauthorized access. This is the baseline: If the data isn’t secure, nothing else matters.

It will be difficult—if not impossible—for any leader to incorporate AI into their business model unless you and your teams trust AI to operate in a seamless, secure fashion. If the data is wrong, or the orchestration breaks, AI will go haywire... and humans will lose trust in it. 
 

At Calix, we have been working on incorporating agentic AI layers for our Broadband Platform since November of 2023. Because we have a platform, we can deliver these layers securely, unlike other approaches that just dress up a generic AI model and deploy it in a company’s environment. We can keep broadband service provider (BSP) data private, equip agents with the right knowledge, orchestrate processes, and enforce governance.
 

That’s why our layered model is trustworthy. It ensures security, accuracy, stewardship, and trust all work together. So, when AI acts on your behalf, it does so responsibly.