The challenge with rolling your own agent

It has never been easier to create a great demo AI agent. Grab a large language model (LLM) and an agent framework. Choose the vector database to use. Wire up some tools for function calling. And it’s up and running. But this short “time to demo” masks the challenge of building agents that can reliably take action without supervision, safely represent a brand, and operate at the scale needed to handle millions of customer interactions a year. In fact, building an agent is a lot like exploring an iceberg. Above the waterline, it’s clear. Below? A vast, murky underworld. It’s why many companies who began building their own agents ultimately turned to Sierra to get the job done.

What’s lurking beneath the surface?

There is so much to get right, and it takes an enormous investment to:

• Reliably orchestrate complex processes and navigate multi-step workflows;
• Securely integrate with systems like CRM, order management, or your own homegrown tools;
• Enforce guardrails—and provide monitoring and auditing—to ensure compliance in a system where the same inputs don’t always generate the same outputs;
• Build tooling for reporting and analytics, and to review conversations; and
• Maintain an agent through your ongoing software development life cycle, from release management to model migrations.

A lot of companies don’t have the AI talent to build their own agent, and the many that do increasingly question whether it’s worth the schlep. “Why not invest those resources in improving our core product?” they ask, “especially if we can work with a company that’s done this hundreds of times, with all the associated lessons compounded over time."

Build with Sierra to avoid the hidden complexity

Sierra’s Agent OS empowers any developer, from experienced AI engineers to generalists, to build and scale powerful agents fast.

Trust built in

• Guardrails—and the ability to dial up or down those rules—ensure agents behave appropriately, especially in regulated environments.
• PII detection and encryption protect personal data by default.
• Auditing tools and role-based access control provide enterprise-grade governance and traceability from day one.

Performance at scale

• A constellation of models delivers significantly higher performance than agents which rely on a single one.
• Parallelism—the execution of multiple tasks or operations simultaneously—makes it possible to handle multi-step workflows and voice interactions with minimal latency.
• Model redundancy and automatic failover ensures service continuity.

Rapid iteration and testing

• User simulation helps explore edge cases and stress-test agents before launch.
• Regression testing enables seamless model upgrades and prevents agent changes from breaking past functionality.
• Release management supports staged rollouts, quick rollbacks, and safe iteration.

Brand, channels, and UX

• Multimodal support—one agent, many channels—enables chat, voice, SMS and more.
• Custom pronunciation keeps voice agents polished and on brand.
• Voice activity detection models suppress background noise and secondary conversation to ensure smooth, natural conversations.
• Continuous evaluation combines automation and human review to ensure quality.

Support for engineering and customer experience teams

• Agent SDK—platform as a service for building agents with code.
• Agent Studio—no code tool enables anyone to build and maintain agents.

The bottom line

By building with Agent OS, companies like ADT, Sirius XM, DIRECTV, Clear, Bissell, and Minted avoid the iceberg problem. They can focus on what’s most important—serving customers and growing their business—and let Sierra manage what’s lurking beneath the waterline.

Better customer experiences are built on Sierra. Want to learn more? Let’s talk.