AI Agents

Harness artificial intelligence to automate audience engagement.

AI agents, formerly known as assistants or chatbots, are the first line of support to triage, answer, and solve your audience's questions, issues, and concerns. All chats begin with an AI agent.


Chatbot vs LLM

Scripted chatbots have long been a staple in customer service automation, preceding the modern large language models (LLMs) that have followed. LLMs introduced important advancements for audience engagement, like unstructured conversations and the voice and vision modalities.

Yola marries the best parts of each technology:

Capability
Chatbots
LLMs
Yola AI Agents

Content privacy

Scripted chats

Runs in messaging apps

Human handoffs

Trainable on resources

Unstructured chats

Voice chats

Realtime language translation

How Agents Work

YolaAI

The combination of features from traditional chatbots and modern LLMs is thanks to YolaAI, our natural language processing (NLP) engine. It assesses all inbound messages at runtime, using sophisticated NLP pattern recognition to match the player's conversational intent with an appropriate action in the agent's scripted flow.

If there is no match, and a specified LLM has been enabled for the agent, YolaAI will send the message through to the LLM for an informed, organic response.

By empowering your AI agents with both a scripted flow and a trained, personalised LLM, you'll have an incredibly robust, well-rounded bot at your disposal.


Chat Flow

AI agents are interacted with through realtime messaging, and can be configured to handoff chats to a human. For human-to-human emailing, see tickets.

AI agents run this sequence for every inbound text or voice message received:

1

Receive message

A message is received through one of the AI agent's enabled messaging apps or website interfaces.

2

Check triggers, apply actions

YolaAI checks for scripted triggers from a system event or which match the player's intent, and applies corresponding actions—like a stock reply or human handoff. The flow ends here if any trigger/action is applied.

3

Use Fallback trigger (if enabled)

The Fallback trigger is a catch-all for unmatched conversational intent. It will intercept the message and reply with a scripted response, ending the flow.

4

Use LLM (if enabled)

If nothing was triggered in steps 2 & 3, the message is sent to a specified LLM, which generates an unstructured response. LLMs can be trained and personlised for your use case.

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