Agentic NLU AI engine

Introducing Agentic NLU: Vonage’s most advanced AI engine yet

Powered by cutting-edge NLU and autonomous agents, Vonage's Agentic NLU AI engine enables virtual assistants to understand context, make decisions, and take action, turning static scripts into intelligent, goal-driven conversations.

Gated feature

Agentic NLU is currently a gated feature. For more information, please contact Vonage Support or your Account Manager.

How an AI Agent works

To fully leverage Agentic NLU, it is essential to understand that an AI Agent operates in a continuous loop of perception, planning, and execution.

1

Perception

The agent observes the user’s input.

Example input: "I want to fly to London tomorrow."

2

Goal identification

The agent identifies the user's specific objective based on the input received.

Goal: Book a flight to London for tomorrow.

3

State evaluation

The agent assesses what it knows vs. what it needs.

Current State: Missing departure time.

4

Planning

The agent generates a sequence of steps to reach the goal.

Steps: Ask for time → Search flights → Offer options → Confirm booking

5

Action selection

The agent selects the next best move based on the plan (e.g., asking a question or calling an API).

Action: “What time would you like to depart tomorrow?”

6

Execution

The agent acts and updates the state.

User response: “Evening.”

7

Goal check

The agent checks if the goal is met.

• If the goal is met, the agent marks the goal as completed.

• If the goal is not met, the agent loops back to Step 4 to refine the plan.

Why Agentic NLU is unique

Pro Tip

If your current flows rely heavily on complex NLU mapping and repetitive input nodes, switching to Agentic NLU can significantly reduce your maintenance burden.

Agentic NLU allows virtual agents (VAs) to engage in dynamic, natural interactions without relying on predefined flows. It is designed to provide human-like intelligence at every touchpoint while maintaining the reliability of deterministic actions.

For builders: Effortless configuration

For the VA builders, Agentic NLU represents a shift from explicit logic mapping to objective-oriented configuration:

• De-cluttered flow design: A single Agentic Capture node can replace multiple Collect Input nodes. Additionally, Agentic nodes have the entire communication layer (Speak & Listen) preconfigured and running in the backend.

• Low training overhead: Define intents with a simple description and a few examples rather than hundreds of user expressions.

• Instant configuration: Add or update intents and parameters instantly using natural language instructions (LLM prompts), drastically reducing manual training data requirements.

• Robust voice handling: Built-in fuzzy matching automatically corrects Automatic Speech Recognition (ASR) transcription errors, preventing unnecessary call failures.

• Pre-configured governance: Each Agentic node includes built-in system instructions, goals, safety guardrails, and memory configurations.

• End-to-End Agentic Architecture: The Agentic NLU Template enables you to route 100% of callers' conversations via AI Agents, providing human-like intelligence at every stage of the interaction.

For callers: An intuitive experience

Agentic NLU creates an authentic conversational experience where callers feel understood, not just processed.

• Natural, free-flowing dialogue: Callers speak naturally without needing to follow a script or specific keywords.

• Multi-parameter capture: An Agentic node can extract multiple pieces of information (e.g., date, destination, and time) from a single caller utterance.

• Dynamic context switching: If a caller shifts topics mid-flow, the Agentic node recognizes the change request and transitions smoothly to the new topic without restarting the conversation.

• Automatic contextual responses: The engine generates smart replies to handle ambiguity or invalid input, providing corrective guidance without manual scripting.

• Proactive frustration detection: Identifies escalation triggers based on repeated failures or unmet goals before a caller even asks for a human.

Agentic NLU limitations

• Available for voice agents and English language only.

• An Agentic Response is automatically generated by LLMs and cannot be manually defined by Studio users.

• Retry behavior and reconfirmation behavior are predefined and cannot be altered manually.

• When using Agentic NLU, AI engine selection becomes fixed and cannot be changed.

• Agentic NLU uses a different set of components and architecture from Hybrid NLU or Traditional NLU, preventing cross-engine migration.

  • Once an agent is created with Agentic NLU, it cannot be switched to another NLU engine.

  • Agents built with the Hybrid NLU or Traditional NLU engines cannot be imported or duplicated into Agentic NLU.

  • Similarly, agents built with Agentic NLU cannot be converted to Hybrid NLU or Traditional NLU.

• Agents built on the Agentic NLU engine are not supported by Virtual Assistant Historical Analytics. To capture and report session data, use Traditional NLU or Hybrid NLU.

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Understanding the architecture: Agentic nodes

Agentic NLU relies on two specialized AI Agents implemented as Agentic nodes. These nodes structure how conversations are handled, ensuring that every caller's input is evaluated and routed appropriately through a continuous reasoning loop, enabling fluid, goal-driven interactions.

Agentic Classification node (The Hub)

As the Hub, this node serves as the assistant's central brain. It identifies the caller's intent or topic at each stage of the conversation and routes the flow accordingly. It includes key functionalities such as:

• Intent recognition: Classifies the caller's input against Agentic Intents to determine the most relevant intent or topic.

• Global Intent support: Recognizes and processes global intents (e.g., “Cancel,” “Talk to agent”) when configured.

• LLM-powered matching: Employs large language models (LLMs) for prompt-based classification, requiring minimal training data.

• Outcome handling: Delivers one of three outcomes, Success, Missed, or Failed, based on the classification result.

• Intent storage & Agentic fallbacks: Records the detected intent for downstream use. If no match is found, it automatically generates an informed response suggesting available intent options.

• Isolated testing: Features a Save and Test option for standalone classification performance testing during configuration.

Agentic Capture node (The Branch)

As a Branch, this node functions as the specialist. It is triggered by the Hub to gather the specific data points (parameters) needed to fulfill a chosen intent. It includes key functionalities such as:

• Intent tracking: Continuously monitors the active intent and recognizes context changes (e.g., switching to a new intent during a conversation).

• Multi-parameter capture: Acquires multiple parameters within a single process when required to complete an intent.

• Retry handling: Facilitates parameter retries to ensure accurate value capture in the desired format.

• Reconfirmation support: Validates captured parameter values with callers, particularly valuable in voice interactions where ASR errors occur frequently.

• Agentic responses: Automatically produces contextual replies to address incomplete or incorrect caller's inputs, maintaining conversation fluency.

• Proactive escalation detection: Detects potential escalation scenarios during interactions and pinpoints the specific parameter that triggers them. Escalation is managed automatically — no additional setup needed.

The hub-and-branch loop

The conversation remains within the Agentic Capture node (the Branch) until the task is complete or an escalation is triggered. If the AI Agent detects a topic shift, it triggers an “Intent Change” scenario. This seamlessly routes the caller back to the Agentic Classification node (the Hub) to re-evaluate the request and re-orient the flow.

For instructions on creating and configuring an Agentic NLU agent, see:

Building an Agentic NLU agent

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Reports: Call logs

Every conversation is logged and visible under Reports. Call logs show input, detected intent, captured parameters, and, if capture failed, the reasons for escalation. You can use call logs to improve coverage and adjust flows.

The Call log modal consists of the following elements:

• Session Information: Summarizes essential call details: agent name, event type, caller and agent phone numbers, date and time, and session ID.

• Transcript: Contains the full agent-caller conversation transcript, clearly indicating who spoke and when.

• Parameters: Displays all parameters captured during the call, showing exactly how input was processed and where it succeeded or failed.

• Flow Path: Displays a complete trail of every node triggered during a call. Additionally, it exposes the underlying Communication Layer to ensure full transparency:

  • It displays a count of the total number of nodes used from the Communication Layer across all conversation loops.

  • Every node activated throughout the session is documented, showing the specific order of the "Perceive-Plan-Act" cycle.

  • You can access the Input and Output details for each node within the loop by selecting the "Show More" option.

  • It provides the status code for every triggered node, confirming successful execution at each step.

To access call logs, perform the following steps:

1. Navigate to Reports in the black ribbon at the top of the screen.

2. Select Reports from the dropdown menu.

3. In the Generate Report section, select Call Log as the Report type.

4. The session list updates automatically. Click the session ID of the session you wish to inspect.

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Migration and Compatibility

To duplicate your Agentic NLU agent, perform the following steps:

1. Find the agent you want to duplicate.

2. Click More Actions (⋮) and select Duplicate.

3. Edit the fields available in the Duplicate Agent modal.

4. Click Duplicate Agent to save your new VA.

To import an Agentic NLU agent, perform the following steps:

1. Click Import Agent on the right-hand side.

2. Upload or drag and drop the zip file with your agent.

3. The fields in the Import Agent modal autopopulate with the agent's information. You can check and edit them if required.

4. Click Import Agent to save your new VA.