PyData Boston 2025

Specialists are vital in enterprise sales, but their expertise is stretched thin. At Red Hat, our solution architects and product experts — highly skilled resources essential for winning complex deals — were often engaged on recurring questions from account teams. While this demonstrated their importance, it also highlighted an opportunity: how could we scale their knowledge more broadly, without relying on one-to-one interactions?

To address this, we developed an AI-powered agent designed to provide on-demand, sales-ready knowledge and accelerate deal progression. The first iteration focused on surfacing accurate responses from curated internal knowledge sources, including product documentation, knowledge base articles, Red Hat’s Content Center data, and shared repositories such as Google Drive. This reduced inbound questions and freed specialists to focus on high-value opportunities.

But knowledge alone wasn’t enough. Sellers also needed contextual intelligence and deal progression support. In the next phase, we extended the agent with a tool-calling framework (Model Context Protocol), enabling it to pull in live account insights from external revenue intelligence systems (e.g. People.ai). We further integrated quoting tools, booking sheets, and normalized account mapping tables — allowing the agent not only to answer “what” and “why,” but also to support sellers with pricing and quoting actions directly within their workflows.

The result is a multi-tool AI agent that accelerates revenue while improving consistency and trust. In this talk, we’ll share the architecture, design decisions, and evaluation metrics behind this evolution. Attendees will learn practical patterns for moving beyond static RAG bots into workflow-integrated agents that scale scarce expertise in any domain.