We sat down with Brad Klingenberg, Naro's Chief Algorithms Officer, to get a plain-English breakdown of the concepts reshaping how sales and marketing teams work: AI agents, automation, model context protocol, and more.
Language Models: The Foundation
Language models are neural networks that process text input to generate text output. They're the foundation for tools like ChatGPT, Claude, and Gemini — and increasingly, for purpose-built tools like Naro.
Agents vs. Standard Chatbots
Agents differ from standard conversational models in two critical ways: tools that enable external actions (web searches, database connections) and reasoning capabilities that allow adaptive, multi-step problem-solving rather than rigid workflows.
A chatbot answers a question. An agent figures out what steps are needed to get to an answer — and takes them.
Automation vs. Agents
The distinction centers on flexibility. Automations are rule-based — they follow predefined steps without deviation. Agents demonstrate adaptability by reasoning through outputs and adjusting their approach dynamically.
Think of automation as a flowchart. Think of agents as a thoughtful colleague who can handle the unexpected.
Context Engineering
Context engineering means providing models with appropriately formatted, relevant information needed for success. It encompasses data selection, retrieval, and combination strategies — going well beyond simple prompt design.
As Brad puts it: "Getting the right information to the model at the right time, in the right format" is the fundamental challenge — and the fundamental opportunity.
What MCP Changes
MCP (Model Context Protocol) functions as an open protocol that standardizes how language models call tools. Before MCP, connecting AI to external systems required custom integrations for every model-tool combination. MCP democratizes tool integration — build it once, use it anywhere.
RAG in Context
Retrieval-Augmented Generation is one context-engineering method: retrieving relevant documents or data to supplement the model's knowledge base before it generates a response. It's how AI tools can answer questions about your specific company, your calls, and your content — rather than relying solely on what they learned during training.
How the Naro Agent Works
The Naro Agent orchestrates multiple specialized sub-agents to analyze conversations and content, providing insights, identifying gaps, and generating actionable outputs for marketing and sales teams.
It's not one model doing everything — it's a coordinated system where each component handles what it does best.