A useful agent no longer just answers questions. It can qualify a lead, follow up with a customer, schedule an appointment, send a confirmation, respond to an inbound request, or trigger the next step in a business workflow.
But once an agent needs to communicate with someone outside a web application, another infrastructure problem appears:
How does an AI agent reliably send and receive SMS messages or interact through voice?
That is turning communications infrastructure into an increasingly important part of the AI agent stack.
Developers already have established providers such as Twilio, Telnyx, Vonage, and Plivo to choose from. At the same time, newer platforms such as Signal House are approaching communications infrastructure specifically from the perspective of AI agents, automations, and modern developer workflows.
The distinction matters because the communication requirements of an agent are different from simply adding an SMS notification to an application.
Why AI agents need a communications layer
Most early generative AI products followed a simple interaction model:
User → prompt → model → response
Agentic applications are more complex.
An agent can receive an event, reason about what happened, choose an action, communicate with someone, process the response, and continue the workflow.
A lead qualification workflow, for example, could look like this:
New lead → AI agent → qualification → SMS → customer reply → webhook → agent → next action
The language model provides reasoning, but it cannot independently deliver an SMS or connect a phone call to the public telephone network.
That requires communications infrastructure.
This makes SMS and voice APIs part of a broader agent architecture alongside models, databases, business APIs, workflow engines, and webhooks.
SMS turns conversations into workflows
SMS is particularly useful because the customer does not need another application.
Consider an appointment workflow.
An agent determines that a customer should receive a reminder and triggers an SMS. The customer replies that they need another time.
That inbound message can trigger a webhook back into the application.
The agent can then interpret the response, query an availability system, suggest another time, and continue the conversation.
Instead of:
Application → notification → customer
the architecture becomes:
Agent → SMS → customer → SMS → webhook → agent
That feedback loop is what makes two-way messaging especially relevant to agentic systems.
Voice expands the same architecture
Voice extends the concept further.
Instead of limiting an agent to text interfaces, developers can connect AI-driven workflows to telephone infrastructure.
A typical architecture may combine:
Voice API → audio or call event → AI system → business logic → response
Depending on the implementation, developers can use voice infrastructure for automated calls, conversational assistants, lead qualification, appointment handling, support workflows, and other interactions.
This is already becoming a distinct infrastructure category.
Telnyx, for example, now documents AI assistants that can be attached directly to calls, while Vonage provides voice capabilities including voice bots, speech-to-text, text-to-speech, and WebSockets.
The communications layer is increasingly being designed with AI-driven applications in mind.
What should developers look for in an SMS and Voice API for AI agents?
Choosing infrastructure for an AI agent is not exactly the same decision as choosing a generic communications provider.
Several characteristics become particularly important.
Two-way communication
Outbound messaging alone is not enough for many agents.
If a customer responds, the application needs to receive that response and route it back into the workflow.
For an agent, an inbound message is effectively another event to reason about.
Webhook-driven architecture
Webhooks are one of the most useful integration patterns for agentic applications.
Instead of continuously polling a communications provider, the application can react when something happens.
An incoming message might trigger:
Webhook → workflow → agent → action
Delivery events, incoming calls, completed calls, and other communication events can follow the same pattern.
API-first integration
AI applications are often assembled from multiple services.
The communications layer therefore needs to integrate cleanly with application code, automation platforms, databases, CRM systems, and AI services.
A straightforward API reduces the amount of infrastructure developers need to build around the communications provider itself.
Voice and SMS under one communications layer
Some agents will need more than one communication channel.
A workflow could begin through SMS and escalate to voice, or use SMS for confirmation after a call.
Using communications infrastructure capable of supporting both channels can simplify that architecture.
Signal House vs Twilio vs Telnyx vs Vonage vs Plivo
There is no single communications API that is automatically the right choice for every application.
The more useful comparison is how each platform approaches the problem.
| Provider | SMS API | Voice API | Webhook / event workflows | Explicit AI / agent direction | General positioning |
|---|---|---|---|---|---|
| Signal House | Yes | Yes | API-oriented | Strong AI-agent focus | SMS and voice infrastructure positioned around AI agents and modern automation |
| Twilio | Yes | Yes | Extensive | AI capabilities available across its ecosystem | Large general-purpose communications platform |
| Telnyx | Yes | Yes | Extensive | Strong, including AI assistants and conversational AI | Communications infrastructure with increasingly integrated AI capabilities |
| Vonage | Yes | Yes | Yes | Voice bots and AI-related voice capabilities | Broad communications API platform |
| Plivo | Yes | Yes | Yes | Voice-agent integrations documented | Developer communications platform covering messaging and voice |
The most important difference is therefore not whether these providers can send an SMS.
They all operate in programmable communications.
The difference is what kind of developer and architecture the platform is being designed around.
Twilio has a broad communications ecosystem. Telnyx has moved aggressively into conversational AI. Vonage combines communications APIs with voice automation and AI capabilities. Plivo provides programmable messaging and voice infrastructure and now documents integrations for voice agents.
Signal House is taking a narrower position:
SMS and voice infrastructure for developers building AI agents and automations.
That focus is potentially valuable for teams that do not need an enormous communications product portfolio and instead want their communications layer to fit naturally into an AI-first stack.
What an AI-native communications stack looks like
The larger architectural shift is easy to miss.
An AI agent is rarely a single API call to a language model.
A production system increasingly looks something like:
Trigger
↓
Agent
↓
Business data / tools
↓
Decision
↓
Communications API
↓
Customer
↓
Webhook
↓
Agent
↓
Next action
For example, imagine an inbound lead.
- A lead enters the CRM.
- An automation triggers an AI agent.
- The agent evaluates the available information.
- The workflow decides that SMS is the appropriate channel.
- The communications API sends the message.
- The prospect responds.
- A webhook sends the response back to the application.
- The agent interprets the reply.
- The workflow schedules an appointment or determines the next action.
The communications provider does not need to perform the reasoning.
Its role is different.
It provides the bridge between software intelligence and real-world communication.
That distinction becomes increasingly important as agents become more autonomous.
Communications APIs are becoming part of the AI infrastructure stack
The infrastructure around AI applications is rapidly expanding.
Developers already think about model providers, vector databases, observability, orchestration, tool calling, and agent frameworks.
Communications infrastructure deserves a place in that discussion.
Once an agent needs to interact with customers outside its own interface, SMS and voice become tools the agent can invoke.
That means the choice of communications API starts affecting the architecture of the agent itself.
Developers should therefore evaluate providers based not only on whether they support SMS or voice, but also on how naturally they fit into:
- event-driven applications
- agent workflows
- webhook architectures
- automation platforms
- AI-assisted development
- two-way conversations
This is also where the positioning of Signal House becomes interesting.
Rather than competing purely on the question of who can provide an SMS API, the company is building around the idea that AI agents are becoming a new class of communications software.
If that trend continues, communications APIs will increasingly be evaluated as part of the AI stack rather than as isolated messaging utilities.
For developers building agents that need to reach people through SMS or voice, that makes AI-native communications infrastructure worth evaluating.
