Agentic Virtual Agents Powered By LAM

Most automated engagements today are built to answer, not to act. Today’s virtual agents automate tasks, not outcomes. Without context, reasoning, and orchestration, they frustrate customers, fragment journeys, and erode trust in AI. When automation can’t adapt, experience can’t evolve.  

Most organizations today rely on automation that is reactive, fragmented, and limited to simple interactions:

• Automation answers questions but cannot complete tasks end-to-end 

• Customer journeys break due to siloed systems and lack of context 

• Bots fail when interactions go off-script, requiring human escalation 

• Increasing customer expectations demand resolution—not just engagement 

The results of all of this includes higher cost-to-serve, lower containment, and declining trust in self-service.

Agentic Virtual Agents enable contextual, goal driven autonomous automation. It understands goals and reasons dynamically to achieve outcomes. It can plan, sequence, and execute multi-step actions autonomously within guardrails. It integrates deeply across systems, channels, and human teams for end-to-end orchestration. It operates proactively, anticipating needs, detecting issues, and taking guided action.

Agentic Virtual agents understand intent, can reason through context, and independently determine the best way to achieve customer outcomes within defined guardrails. It can adapt mid-conversation—switching topics, resolving ambiguity, and pulling knowledge from multiple sources for consistent, human-like dialogue. It acts safely and predictably with built-in policies, compliance controls, and transparency—ensuring AI remains aligned to business intent. It improves over time through interaction insights, feedback loops, and analytics to enhance accuracy, containment, and customer satisfaction.

Agentic Virtual Agents transform automation by enabling:

1. Autonomous Goal Execution

• Understand customer intent and desired outcomes

• Plan and execute multi-step workflows

• Dynamically adapt to changing conditions

2. Cross-System Orchestration

• Connect across CRM, billing, service, and back-office systems

• Execute actions using APIs, tools, and workflows

• Maintain context across channels

3. Governed and Safe AI

• Built-in guardrails and compliance controls

• Deterministic execution (reduced hallucinations)

• Full auditability and explainability

4. Continuous Learning and Optimization

• Improve performance through feedback loops

• Optimize workflows and decisioning over time

These capabilities are powered by LAMs, which are designed to take action—not just generate responses.

Agentic Virtual Agent is the evolution of Genesys’ conversational AI, from reactive automation to autonomous engagement to deliver consistent, outcome-driven customer experiences at scale. 

Powered by LAM, they act independently within defined guardrails to resolve complex requests end-to-end, orchestrate tasks across systems, and adapt dynamically to each customer’s intent and context.

Built on LAMs that reason, plan, and act with context, AVA transforms AI from response generation to outcome delivery. Its adaptive orchestration learns across every channel and workflow, continuously improving performance and efficiency at scale. Grounded in explainability and governance, AVA empowers enterprises to deploy autonomous systems that act responsibly, align with policy, and deliver measurable business results.

A customer contacts a company to dispute a charge on their account.

Traditional Experience

• Customer interacts with a bot that provides limited answers

• Customer is transferred to an agent

• Agent must gather context again

• Resolution requires coordination across systems

With Agentic Virtual Agents

• AVA understands the customer’s goal: “dispute a charge”

• Retrieves transaction details

• Validates eligibility based on policy

• Initiates dispute workflow

• Updates backend systems

• Confirms resolution to the customer

All of this occurs within a single interaction, without human intervention unless necessary.

This represents a shift from Conversation-based automation to Outcome-based automation

Agentic Virtual Agents are autonomous digital workers that can plan, adapt, and execute end-to-end workflows, delivering complete outcomes across channels without human intervention.

Powered by Large Action Models, they handle multi-turn conversations, retain context, and produce deterministic, low or no-hallucination decisions, enabling reliable, real-world autonomous engagement.

They are designed in Genesys Cloud AI Studio, where teams configure their tools, knowledge connections and guardrails, ensuring safe, controlled and enterprise-ready autonomic behavior.

Agentic Virtual Agents enable organizations to:

• Automate complex, multi-step workflows

• Deliver end-to-end resolution in self-service

• Reduce reliance on human agents for routine and complex tasks

• Maintain governance, compliance, and control

AVA bridges the gap between customer expectations and current automation capabilities, enabling scalable, intelligent CX.

Business Logic

Large Action Models

Agentic Virtual Agent uses Large Action Models (LAMs) to allow end-to-end workflow execution with self-planning and self-correcting capabilities.

• Model reasoning based on instructions and available tools and knowledge

• No-code descriptions of VA behavior broken down into easy to consume segments

• Purpose trained model drives lower hallucination and better reasoning capabilities between tools

MCP/A2A Connectors for Virtual Agents

MCP connects agents to tools and data; A2A connects agents to each other—together enabling scalable, multi-agent workflows.

• MCP lets AI models reliably access real tools and data through a shared, secure protocol.

• A2A enables AI agents to communicate and collaborate with each other to complete complex tasks from end to end.

Improved Continuity Cues

Manage conversational UX by configuring how to represent the bot thinking or taking longer actions

• Control the perception of the conversation quality through micro responses that inform the user what the bot is doing

• Provide contextually relevant and highly empathetic fillers when facilitating actions on behalf of the user

• Provide visual cues when applicable to indicate processing times

Flow Miner

Create AI Guides from Conversation Transcripts

• Quickly turn real conversations into usable automation assets

• Designed for reliability at scale

• Human-in-the-loop by design

• All outputs are drafts—fully reviewable, editable, and publishable only with explicit author approval.

• Seamlessly integrated into AI Studio

• Flow Miner fits naturally into existing Guide creation workflows, with no new learning curve required.

Customer Conversation Summaries

Admins can shape interaction summaries to fit business needs.

• Align with organizational style – Match the way agents naturally write Designed for reliability at scale

• Optimize for business needs – Highlight the most relevant information for better decision-making

• Fine-tune content and structure – Control length, formatting, PII redaction, entity extraction, currency presentation, and selective exclusion of conversation parts

Distribution Logic

• AVA is deployed across voice and digital channels (chat, messaging, IVR)

• Interactions are routed based on:

• Customer intent

• Channel availability

• Workflow complexity

• AVA handles interactions autonomously when:

• The workflow is within defined guardrails

• Required tools and integrations are available

• Escalation to human agents occurs when:

• Exceptions arise

• Compliance requires human intervention

• Customer explicitly requests an agent

For human agents (when escalation occurs):

• Full conversation history and context is preserved

• Visibility into:

• • Actions taken by AVA

• • Data retrieved and updated

• • Workflow status

• Suggested next steps and recommendations

AVA reduces agent effort by ensuring agents only handle:

• Exceptions

• Complex decision-making

• High-value interactions

Real Time Reporting

Supervisors and operations teams can monitor:

• Active AVA interactions

• Workflow execution status

• Escalation rates

• Containment rates

• System and tool performance

Real-time observability ensures:

• Immediate visibility into AI behavior

• Rapid issue detection and resolution

Historical Reporting

Organizations can analyze:

• Automation and containment rates

• Resolution times

• Customer satisfaction metrics

• Workflow success/failure rates

• Operational cost savings

Insights enable:

• Continuous optimization of workflows

• Identification of improvement opportunities

• Measurement of ROI from autonomous automation

• Customers prefer self-service that resolves issues fully

• Organizations operate across multiple systems and channels

• Automation must be:

• • Scalable

• • Reliable

• • Governed

• AI must operate within defined guardrails and policies

• Not all interactions can be fully automated; escalation remains necessary

To successfully implement AVA, customers must:

• Define clear business goals and use cases

• Configure:

• • AI tools and integrations

• • Knowledge sources

• • Guardrails and policies

• Ensure data availability across systems (CRM, billing, etc.)

• Validate workflows and business logic before deployment

• Monitor performance and continuously optimize

Additionally, organizations should:

• Align stakeholders across CX, IT, and AI governance

• Establish compliance and risk frameworks

• Train teams on AVA capabilities and operational model

V 1.0.0 Last Updated 04/17/2026