Regal Launches Copilot For The Complete AI Agent Lifecyle

Today, AI Voice Agent platform Regal announced Copilot, a virtual agent builder tool that leverages AI and existing interaction data to automate the creation, testing, monitoring, and continuous improvement of customer-facing AI agents. Copilot compresses AI deployment timelines from months to weeks, providing cost savings while eliminating complex prompt engineering and manual AI agent configuration.

Check Out Our Interview With Regal CEO Alex Levin Here

How Regal Copilot Works

Using data from live customer conversations, integrated third-party tools, Regal’s Unified Customer Profile, AI Routing, AI Agent Frameworks, and Observability, Copilot creates scalable, production-ready AI agents complete with built-in guardrails and capable of handling multi-step interactions.

Users can write a description of what the AI agent needs to be able to do, specify use cases, or use past call recordings and scripts to enhance prompts. Because Copilot integrates with third-party knowledge bases, CRM systems, and CCaaS platforms, prompts are automatically personalized. 

Copilot instantly creates the agent, incorporating empathy, brand voice, business logic, and compliance requirements into the process from the ground up.

Teams can further customize the AI agent's accent, language(s), emotion, voice speed, temperature, volume, responsiveness, and interruption sensitivity. They can also specify escalations, handoffs, voicemail rules, and map workflows via Regal’s no-code, drag-and-drop interface.

Agent Testing and Deployment in Regal Copilot

Once agents have been created, Regal’s Copilot tests them before go-live. Copilot automatically creates call scenarios and conversation simulations to test edge cases, objection handling, regression, conversation flows, and rule-break requests.

Once a test is complete, Copilot automatically provides reason-backed suggestions for improvement agent performance that users can accept with one click. Agent version histories are available.

After deployment, users can run A/B testing, real-time agent conversation monitoring, and live sentiment tracking to monitor AI agent performance. Copilot continuously refines and improves AI agents based on real customer conversations, offering suggestions for improvement and detecting key CX issues early - before they spiral out of control.

What This Means For Enterprises

For the CX and contact center leaders who have spent the past year-and-a-half hearing about  how AI deployment at scale is “only months away,” Regal’s Copilot’s ability to actually shorten AI implementation timelines is welcome news.

Copilot’s combination of data-driven agent creation, automated testing, and continuous post-deployment refinement simplifies the three stages where most enterprise AI projects stall: configuration, complexity, pre-launch QA bottlenecks, and the slow drift in agent performance after go-live.

Strategically, Copilot signals that Regal is interested in owning the full lifecycle of AI agent management, removing the need for the dedicated prompt engineers and AI ops resources that enterprise deployments have historically demanded.

While CCaaS/CX vendors like Salesforce, Genesys, and Five9 are competing aggressively on agentic AI capabilities, Regal's differentiation is specificity: it's not creating a general-purpose agent builder, it's creating the best tools possible for voice AI agents. Regal is betting that enterprises will be willing to pay a premium for that specific expertise - and they’re very likely right.