
September 2023 Releases
Regal is announcing Copilot, our agent for building AI agents. Create, test, deploy, monitor, and improve your Regal AI agents faster than ever by managing an agent instead of going through each step yourself.
Building agents for complex use cases requires expertise that many teams do not have today. After 400 million calls, we know what it takes to make customers successful.
At the same time, the way people interact with enterprise software is changing. We’re shifting from tools that require users to manually configure every detail to systems where you can simply direct an agent to handle the work on your behalf. This is already the expectation in engineering, where developers use agents to move faster and focus on higher-value work.
Copilot brings this shift in work style to CX teams. Simply describe what you want your AI agent to do, and Copilot outlines a plan and executes it to build a production-ready AI agent. Copilot leverages Regal’s Unified Customer Profile, AI Routing, AI Agent Frameworks, and Observability to ensure every agent is scalable, compliant, and has measurable success.
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The best CX teams deeply understand what their customers want and are constantly improving their AI agents to meet those needs. For complex AI Agent use cases, that meant continuously spending time learning from real conversations, adapting to new behaviors, and refining performance over time. Now, Copilot makes this process quick and seamless. It learns from live interactions, surfaces improvement opportunities, and allows teams to make changes faster, with confidence.
With Copilot, you have a CX expert at your fingertips, capable of building and iterating AI agents for multi-step use cases involving custom actions, knowledge bases, integrations, and personalization. Instead of maintaining agents, you can focus on understanding customers, expanding use cases, and driving better outcomes with every interaction.
With Copilot, you start with assets you already have, whether it be a description of your use case, an existing script, or even past call recordings with your human agents, and it does the heavy lifting from there. Your business logic, brand voice, and operational requirements get incorporated into the agent automatically. To get your AI agent production-ready, Copilot generates call scenarios, runs tests, and suggests improvements.
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For example, during testing, Copilot may flag that your agent struggles to stay on topic when callers go on tangents. In turn, it proposes stronger guardrails to keep conversations focused, smooth, and more human. For your team, this means simply approving suggested improvements. Plus, every change comes with Copilot's reasoning behind it, so you stay in control of the outcome without getting buried in the details.
Most teams learn how to build better agents through trial and error. Copilot lowers that learning curve by applying best practices directly into the build process, drawing on patterns developed across hundreds of millions of real customers calls. Whether you are configuring guardrails, structuring a conversation flow, or tuning a handoff, Copilot applies what works, not what sounds good in theory.
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For example, Copilot incorporates principles like empathy: guiding agents to acknowledge customer context, respond with the right tone, and move conversations forward effectively. It also automatically personalizes prompts using data from your CRM and other integrated systems, so agents can adapt responses based on who the customer is, while still sounding human.
These best practices extend across the full voice experience, including voice configuration, pacing, voicemail handling, and handoffs. Whether you’re configuring guardrails, structuring a conversation flow, or tuning escalation logic, Copilot applies guidance that helps your agent stay compliant and natural, even during the most complex use cases.
Typically, teams start deploying AI agents for low-risk tasks and gradually expand, but the true goal is reaching entirely new use cases: ones that were never possible before due to limited human capacity.

Copilot compresses use case expansion to weeks, not months:
With faster builds, Copilot makes it possible to explore new channels, audiences, messaging, and conversation types without the costly time investment.
For example, you may start with an AI agent that answers simple FAQs about student enrollment. With Copilot, you can quickly expand to a more advanced agent that qualifies new student leads, pulls in data from your CRM to personalize the conversation, verifies eligibility, and books a campus tour within the same call.
The deployment loop shortens, which means your team can act on what is working sooner and expand into use cases that simply were not feasible before.
If you want to see Copilot in action, reach out to our team.
Regal Copilot is an agent for building AI agents. It enables teams to automatically create, test, deploy, monitor, and improve AI agents by managing an agent instead of going through each step manually. Simply describe what you want your AI agent to do, and Copilot outlines a plan and executes it to build a production-ready AI agent. Once deployed, Copilot learns from live interactions, surfaces improvement opportunities, and allows teams to make changes faster, with confidence.
Instead of writing prompts from scratch, you can drop in a call recording or existing script, or simply describe what you want your AI agent to do. Copilot then outlines a plan and executes it to build a production-ready AI agent, including generating call scenarios, running tests, and suggesting improvements.
Copilot learns from live interactions, surfaces improvement opportunities, and enables teams to make changes quickly. It continuously refines agent performance based on real conversations, helping teams adapt to customer behavior and improve outcomes over time.
Copilot leverages Regal’s Unified Customer Profile, AI Routing, AI Agent Frameworks, and Observability to ensure every AI agent is scalable, compliant, and measurable. It automatically incorporates your business logic, brand voice, and operational requirements, while lowering the learning curve by applying best practices from over 400 million calls.
Copilot supports complex, multi-step AI agent use cases involving custom actions, knowledge bases, integrations, and personalization. Teams can start with simple use cases and quickly expand to more advanced workflows like qualifying leads, verifying eligibility, and booking appointments within a single interaction.
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