Sierra helps businesses build more human customer experiences with AI, aiming to bridge the gap between desired customer service and the high cost of delivering it
The company works with hundreds of customers and will serve hundreds of millions of consumers this year
Clients include major brands such as ADT, SiriusXM (with their Harmony AI agent), and large mortgage originators, as well as tech companies
Sierra believes AI-powered customer-facing agents will be standard for all companies in the future, evolving beyond websites and mobile apps
The AI architect is a new role similar to the old “webmaster,” focusing on company AI agents that represent the brand digitally
The role has emerged organically among Sierra’s clients, especially within customer experience teams
It includes three core aspects: understanding AI technologies, acting as a brand ambassador (shape the agent’s voice, tone, persona), and driving business outcomes
AI architects typically come from customer experience, engineering, or retail backgrounds; those close to customer service are most common and often most effective
Traits and Strategies of Successful AI Architects 06:04
Key traits include curiosity, risk tolerance, a willingness to experiment, and a focus on solving real customer or business problems
Successful teams avoid perfectionism and start with small, concrete problems, iterating from initial successes
Effective organizations rethink (not just translate) traditional team structures for the AI era, often creating new roles to coach and refine AI agents based on customer interactions
Example: Some teams now review hundreds of AI-customer conversations daily, continuously improving the agent’s empathy and decision-making
Build vs. Buy Decisions and the Agent Iceberg 09:04
Many technical teams underestimate the complexity of building a robust AI agent platform, focusing only on surface-level elements (model selection, integrations)
Sierra illustrates this complexity with the “agent iceberg” concept: beneath simple choices lie substantial challenges (testing, model migration, voice handling)
Sierra’s Agent OS combines a technical platform for developers and no-code tools for business users, both needed to build effective customer agents
Companies that initially try to build in-house often return after hitting unexpected challenges and delays
Sierra developed a specific “agent development life cycle” for building, testing, and iterating AI agents, given their non-deterministic behavior
Testing uses AI-driven user simulations (with diverse personas and devices) to generate thousands of conversations, identifying weaknesses before going live
Post-launch, coaching tools allow ongoing improvement, letting both CX and engineering teams iterate on agent performance and customer experience
The process includes a feedback loop: agents learn from mistakes, are coached by teams, and improve capabilities over time
The pace of AI advancements is accelerating; new models, frameworks, and benchmarks frequently emerge
Staying current requires consistent immersion in new research, tools, and even adjacent technologies
Hands-on experimentation and proactive forecasting (anticipating where model and hardware capabilities are headed) are essential for maintaining product leadership
Sierra tracks problems that are just out of reach for current models, revisiting them as capabilities evolve to maximize product timing and adoption
The next generation of AI interfaces will move beyond chat and voice, blending text, voice, video, imagery, and interactive user interfaces (“shape shifters”)
The future will likely include AI agents interacting with users through all senses and available channels
Wearables, especially AR glasses, are seen as the ultimate interface for AI assistants, offering persistent, ambient support that is closely integrated into daily life
The long-term vision is for each person to have an omnipresent AI assistant, accessible without relying on traditional smartphone interactions