Specialist Adoption Architect 3

How Red Hat, Inc. built an AI agent for field associates with Red Hat AI. Director, Data Science Principal Machine Learning Engineer Sellers need information in order to do their jobs. At most organizations, this information already exists in various internal systems. The challenge facing every sales team isn't finding that information; it's turning it into action fast enough to matter. To address this challenge, Red Hat, Inc. built Sales Assistant, an enterprise AI agent powered by Red Hat AI and used by Red Hat sellers internally. Sales Assistant surfaces appropriate context, takes action, completes tasks, streamlines workflows, and frees sellers to focus on customers. For Red Hat sellers, critical data lives across Salesforce, product documentation, pricing systems, lifecycle databases, and internal knowledge bases. Each system serves an important purpose, but together they can create fragmented workflows. Preparing for customer conversations often means switching between multiple applications to gather context, validate information, and complete routine tasks. Red Hat sellers didn't lack data or tooling - but navigating disconnected systems produced operational friction. From questions to action in seconds. With Sales Assistant, sellers can interact with enterprise systems using natural language prompts. A seller preparing for a renewal can ask, "What's the renewal strategy for this account?" and receive a grounded, cited response in seconds. Another seller can request, "Generate a quote for opportunity XYZ," and the agent prepares it for human review before submission. Instead of searching across multiple applications, sellers interact through a single interface that connects to the enterprise systems where their data already resides. Behind the scenes, Sales Assistant orchestrates specialized sub-agents that interact with Salesforce, customer engagement platforms, pricing and configuration systems, lifecycle databases, and internal knowledge repositories. Every response is grounded in enterprise data, giving sellers visibility into the sources behind each recommendation. Sales Assistant also maintains conversational context across multiple interactions. Sellers can ask follow-up questions like "What products is this customer currently using?" without repeating previous context, enabling a more natural and efficient workflow. Sales Assistant fits into existing workflows. Sellers can interact with it within the tools they already use, including Salesforce, Slack, a web interface, and a mobile application. Running in production, not in a lab. Sales Assistant is not a prototype or proof of concept. It is a production AI agent processing thousands of requests every day. Running AI at production scale requires more than deploying an LLM. Sales Assistant is built on Red Hat AI using a layered architecture (see Figure 1) designed to deliver scalability, governance, security, and operational reliability. Figure 1. Sales Assistant architecture diagram showing orchestration, model inference, deployment, autoscaling, and observability Users authenticate through Red Hat's single sign-on technology, which is based on Keycloak. After authentication, requests are routed to the Supervisor Agent running on Red Hat OpenShift AI. The Supervisor Agent orchestrates specialized agents, manages tool execution through Model Context Protocol (MCP) servers registered in the Red Hat OpenShift AI MCP registry, and coordinates access to enterprise systems with centralized governance and observability. Red Hat AI Inference handles model inference using a hybrid strategy that automatically selects the most suitable hosted or frontier models for each request, balancing latency, performance, and cost. To ground responses in enterprise knowledge, the platform continuously ingests documents - more than 300,000 so far - using data pipelines built with KubeRay and Docling on Red Hat OpenShift AI. This retrieval-augmented generation (RAG) pipeline grounds responses in current enterprise knowledge. At query time, the platform retrieves the most relevant information, re-ranks results using models served through Red Hat AI Inference, and injects only the highest-value context into the model prompt. The application platform is standardized on Red Hat Universal Base Image based on Red Hat Enterprise Linux, while backend services are built with Quarkus to provide lightweight, cloud-native Java services optimized for Kubernetes. Application promotion across development, staging, and production environments is managed through Kustomize. Horizontal Pod Autoscalers dynamically adjust capacity as demand changes, while observability provides operational visibility across the entire platform. Helping sellers focus on customers. By bringing enterprise data and business actions together in a single conversational experience, Sales Assistant reduces the time sellers spend searching for information and navigating disconnected systems. Sales Assistant isn't yet another tool or system sellers need to navigate; it's an intelligent interface for existing enterprise tools, and helps sellers prepare faster, respond with greater confidence, and spend more time focused on customer conversations. For Red Hat, Sales Assistant demonstrates that enterprise AI is most valuable when it integrates directly into existing workflows while meeting the governance, security, and operational standards required for production. Looking ahead. Sales Assistant continues to evolve as Red Hat expands its enterprise AI platform. The next steps in its development will focus on transitioning to a skill-based architecture, expanding support for the Red Hat Services and Ecosystem portfolios, improving enterprise knowledge management, deepening user personalization, and broadening agent capabilities to enable sellers to complete more tasks directly through the assistant. The platform is also evolving toward more proactive and adaptive AI agents that can anticipate user needs while continuing to operate within enterprise governance, security, and compliance boundaries. Building enterprise AI for production. Enterprises no longer have to prove that AI models are capable of doing real work. Their next challenge is to deploy AI tools that securely connect to enterprise data, integrate with business workflows, and operate reliably at scale. Built on Red Hat AI, Sales Assistant combines orchestration, retrieval, governance, and observability into a production-ready platform for enterprise deployments. As organizations move beyond experimentation, success depends on more than model performance. It requires a platform that provides consistency across hybrid environments, integrates with existing enterprise systems, and delivers the operational controls needed for production deployments. Sales Assistant demonstrates what that looks like in practice. The adaptable enterprise: Why AI readiness is disruption readiness. This e-book, written by Michael Ferris, Red Hat COO and CSO, navigates the pace of change and technological disruption with AI that faces IT leaders today. Mounika is a Data Science Director at Red Hat with over 18 years of experience driving enterprise AI innovation across Security, IoT, and large-scale data platforms. She has a proven track record of leading high-performing teams and delivering AI solutions that transform complex business challenges into measurable outcomes. Passionate about the future of AI, her current focus is on building agentic AI systems and advancing enterprise AI architectures that combine autonomous decision-making with robust governance, observability, and trust. Faisal Shah is a Principal Machine Learning Engineer at Red Hat with over 11 years of experience building enterprise AI products. He works on designing and delivering AI capabilities for enterprise platforms, with a focus on taking ideas from concept to production. His interests include open source AI, agentic systems, and building reliable AI applications at scale. Original podcast

Red Hat announces Sinuhé Sánchez as the new Chief Architect for northern Latin America. - Date published August 11, 2026 The executive joins the Field CTO team, where he will drive innovation strategies and contribute to generating new business opportunities. Red Hat, a global leader in open source solutions, today announced Sinuhe Sanchez as the new Chief Architect for the Field CTO team in Northern Latin America. With over two decades of IT experience, Sanchez will lead the connection between the global Engineering group and the field team for Mexico, Colombia, Peru, and Central America, with the goal of driving innovation strategies, strengthening relationships with strategic clients, and accelerating the generation of new business opportunities. "The widespread adoption of AI has brought companies to a tipping point where they need to maintain business continuity while simultaneously incorporating new technologies. My mission is to support how organizations innovate, ensuring they can evolve their technology strategies with security and freedom of choice, aligning technology with the needs and growth of their businesses," he says. Before joining Red Hat, Sinuhé held technology leadership positions at global software manufacturers, cloud infrastructure and service providers, and consulting and integration firms, including SAP, Dell, and AWS, where he participated in strategic projects for clients in Latin America. His background combines expertise in enterprise architecture, hybrid platforms, artificial intelligence, application automation and modernization, with a focus on generating business value. "Sinuhe will be a key player in achieving the goals of the Field CTO team in Latin America. In his role, he will help bring our customers the full value of our portfolio, our experience, and Red Hat's industry leadership, helping them achieve their strategic objectives and not just meet tactical needs," says Andrea Cavallari, CTO Field for Latin America at Red Hat. The addition of Sinuhé Sánchez reinforces Red Hat's growth strategy in Latin America and reflects the company's commitment to continue supporting organizations in their technological transformation processes, promoting the adoption of open, flexible technologies that are ready for the challenges of the future. More articles. August 11, 2026 - Advertisement - August 11, 2026

4 ways a Red Hat TAM maximizes IT investments, according to Forrester TEI study. Global TAM Practice Lead Modern IT environments are complex. Development and operations teams must constantly balance resolving current infrastructure challenges with planning for scalable, future growth. This requires deep product expertise and technical skills from internal teams that are already resource-constrained. To bridge this gap, enterprises use Red Hat Technical Account Managers (TAMs) as an extension of their teams. A TAM is a single technical point of contact specializing in a specific product family, such as Red Hat Enterprise Linux, Red Hat OpenShift, or Red Hat Ansible Automation Platform. They work alongside your team to prevent downtime, patch system vulnerabilities, and connect you directly to Red Hat engineering experts. How does this partnership actually work in practice, and what does it mean for your business? Red Hat commissioned Forrester Consulting to conduct a 2026 Total Economic Impact(TM)(TEI) study. Forrester interviewed enterprise decision-makers with years of hands-on TAM experience and designed a composite organization ($5 billion global company with 20,000 employees and 20 in-house Red Hat developers) to evaluate the data. What Red Hat, Inc. found was exciting: Investing in a Red Hat TAM delivered a 386% return on investment over 3 years, with a payback period of less than 6 months. The core benefits. The study revealed significant, risk-adjusted financial benefits across 4 major pillars of enterprise operations: * Enhanced time to market ($3.0M accelerated profit): Proactive TAM guidance shortened development cycles. As one Software Solution Architect in the IT industry noted during the interviews, "There's no way we would be at the same level of proficiency with the product [without the TAM]." By helping the team build deep expertise directly on the job, the TAM helped launch new applications a full month faster. * Reduced system outage costs ($1.8M saved): Downtime introduces substantial revenue risk. A Platform Engineering Manager in financial services emphasized the critical role a TAM plays here: "In large-scale environments, the TAM is very important. Critical environments need a fast resolution, and the TAM is the way to achieve this goal." The study quantified this, showing that TAMs reduced the duration of major unplanned outages impact time for the composite organization by 70% by Year 3 while providing log analysis and health checks to avoid minor outages entirely. * Improved developer and IT productivity ($745k labor savings): A TAM deeply understands your unique environment, eliminating back-and-forth ticket friction. With direct TAM guidance, developers saved 40% of their time on Red Hat projects by Year 3, while annual hours spent resolving IT tickets decreased by 75%. * Strengthened security and compliance ($196k reduced risk exposure): TAMs collaborate with internal teams on patching cadences, certificate lifecycles, and vulnerability tracking. This proactive stance directly reduced risk exposure to costly security breaches. The strategic value. Beyond strict financial metrics, enterprise leaders highlighted long-term strategic advantages: * Team upskilling: TAMs deliver hands-on, interactive guidance tailored to your architecture. This allows sysadmins, developers, and platform teams to build new skills directly on their own production architectures. * Advanced product roadmap visibility: Regular meetings provide early insight into upcoming product features. This visibility helps organizations align future technology strategies and share feedback to influence Red Hat engineering. Optimize your investments. Whether you need to justify premium support to executive stakeholders or maximize your existing Red Hat footprint, a Red Hat TAM provides the dedicated expertise required to succeed. Product trial Red Hat Learning Subscription | Product trial. Fill skills gaps and address business challenges by exploring the benefits of Red Hat Learning Subscription trial