Twilio introduces a conversational layer to combine AI and human interaction
Twilio is introducing a new set of platform capabilities to address one of the most persistent problems in the customer experience: conversations that don’t transfer from one to the next.
Announced today at SIGNAL 2026, three new components are a “conversational layer” designed to connect data, channels, and human agents and AI into a single, continuous entity.
The premise is straightforward. Many customer journeys are still fragmented. A user may start in a chat, move to voice, and then follow up by email, repeating the information every step of the way. That disconnect affects conversion, maintenance, and efficiency.
Twilio’s answer is to treat conversations as a continuous system instead of a chain of disconnected events.
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It moves the context forward
This is where its three main components come into play. Conversational Memory, Conversational Orchestrator, and Conversational Intelligence aim to ensure that every interaction starts with context, moves forward, and can adapt in real-time.
- Conversation Memory creates a persistent, identity-resolved profile that combines customer data and interaction history. Instead of treating each engagement as a new beginning, it allows both human agents and AI systems to pick up where the last interaction left off.
- The Conversation Orchestrator manages the flow. It connects communication between channels and manages communication between AI and human agents, effectively combining individual touch points into a single thread.
- Conversation Intelligence adds a real-time layer, analyzing live interactions for signals like sentiment and rising risk, as well as triggering actions while the conversation is happening.
The company hopes that this will allow companies to move from passive engagement to something closer to continuous engagement.
That shift reflects a broader shift in how companies think about AI in the customer experience. The issue is no longer whether AI can respond to customers, but whether it has enough context to respond properly. Twilio’s framework says that the actual bottle is not a model. It is the infrastructure that covers everything around you.
Some new features
The company is also dependent on flexibility. With Agent Connect, developers can connect different AI models or frameworks without rebuilding their communication layer. The platform remains model agnostic, giving teams greater control over how they use AI while avoiding lock-in.
Beyond the core chat layer, Twilio is expanding its channel and platform capabilities. That includes new support for Apple Messages for Business, general availability of Twilio email, and updates to voice AI features such as real-time transcription and intelligent curve detection.
There’s also a redesigned console aimed at simplifying how teams manage complex engagement stacks, with integrated logs, billing, and an embedded assistant.
Examples from first customers point to real-world use cases. Companies use the platform to restore static applications, streamline live conversations with real-time data, and reduce the need for repetitive manual tracking. In each case, the common thread is continuity: carrying the context forward rather than starting over from scratch.
