Genesys has introduced a new virtual agent into Genesys Cloud to improve self-service outcomes for customers.
The solution uses large action models (LAMS), enabling the capability to interpret intent, choose next steps, and carry out actions across systems, adapting to context changes during a customer interaction.
The CX platform provider offers more reliable self-service resolutions by turning conversational AI into a practical, action-oriented tool for customer interactions.
Olivier Jouve, Chief Product Officer at Genesys, argued that for AI to act on behalf of customers in a CX setting, it must be reliable, clear and controlled.
“Autonomy in customer experience only works when it’s built on trust, transparency and control. With our LAM-powered Agentic Virtual Agent, we’re enabling AI to reason, plan and safely take action across systems,” Jouve said.
“This gives organizations a responsible way to move beyond conversations and deliver consistent outcomes customers can rely on.”
Genesys argued that traditional AI assistants have limits when it comes to completing tasks, focusing mainly on generating conversational language rather than making effective, practical solutions.
This includes agent failures when a task requires multiple steps or actions across systems and the agent is unable to reliably drive the correct actions to complete complex workflows.
As a result, traditional AI will often stop at answering a question rather than completing the outcome, creating a gap between conversation and resolution, increasing handoffs and repeat contacts.
These failures can occur when AI models lack built-in governance and clear execution paths, causing agents to produce unpredictable or irrelevant outcomes.
Closing the Gap Between Customer Conversations and Resolution
As an AI-powered digital assistant, the Genesys Cloud Virtual Agent is design to do more than generate conversational responses.
Its LAMs are implemented to understand customer goals, plan steps and take action across different systems and processes to complete work end-to-end, built into the Genesys Cloud platform with enterprise-grade governance, auditability and controls.
The self-service assistant begins by interpreting what the customer wants to achieve, looking beyond what they have said and breaking the goal down into logical steps.
The LAMs then allow the agent to move from simple responses to task execution, such as updating account records, scheduling services, or triggering billing checks across various systems, carrying out work through multiple steps and systems without going back to the customer or department for human intervention.
Changes in conditions can prompt the agent to re-plan its steps and continue the workflow without failures.
Organizations can also define guardrails, permissions and policies for the agent, to ensure actions remain explainable, predictable and aligned with compliance needs.
With more requests being completed end-to-end with fewer handoffs, less repetition and reduced effort, interactions are more consistent and predictable, improving speed to resolution and the overall experience.
How Genesys’s Agent Compares to Traditional LLM-Based AI Assistants
Compared to older models, Genesys’s agent goes further to complete the work required by the customer, functioning as a controlled digital worker rather than a customer FAQ tool.
Compared to LAMs, LLMs generate responses based on probable patterns rather than true reasoning, leading to inconsistencies in behavior when real business logic is applied, due to their inability to execute sequenced, rules-based processes across systems.
Genesys’s AI model can use reasoning to understand the task, select predefined actions and execute them within controlled workflows, and retain interaction context during long conversations, channel switching or changes in reasoning. This helps to improve consistency and reduce failure points.
Generative models may also produce unpredictable outputs, meaning organizations need clear guardrails and audit trails to ensure actions are permissible, decision paths are traceable, and policies and business rules are enforced.
The broader shift from conversational AI as an interface to AI means higher containment, improved resolution rates and reduced customer effort, while maintaining organizational oversight and reliability.
What Are the CX Benefits of Genesys’s Cloud Virtual Agent?
The Cloud Virtual Agent is positioned to make self-service more effective and reliable, reducing customer effort and scaling complex task completion while maintaining predictable outcomes.
The agent supports higher resolution rates by focusing on completing customer tasks across systems, reducing repeat contacts and increasing first-contact resolution without requiring human escalation.
This also decreases customer effort by carrying requests through to completion in one interaction, limiting transfers, repetition and unnecessary navigation.
To reduce dead ends and handoffs, AI agents can handle multistep workflows reliably, lowering breakdowns and reducing the need to escalate to live agents.
This ensures faster outcomes; by executing tasks directly within workflows, it shortens time to resolution and improves overall service speed.
Customers can therefore experience consistent and reliable services as agents operate within governance controls, ensuring predictable, policy-aligned outcomes across customer interactions.
With better continuity across interactions, agents maintain context throughout the journey, reducing repeated explanations and preserving progress across channels.
