{"id":614940,"date":"2026-02-10T10:00:14","date_gmt":"2026-02-10T18:00:14","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/how-large-action-models-will-power-agentic-orchestration"},"modified":"2026-03-13T10:49:10","modified_gmt":"2026-03-13T17:49:10","slug":"how-large-action-models-will-power-agentic-orchestration","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/how-large-action-models-will-power-agentic-orchestration","title":{"rendered":"How large action models will power agentic orchestration"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]It\u2019s 8:00 AM and your flight was cancelled overnight. You open the airline app, already bracing for friction. Instead, you\u2019re greeted by a calm, articulate AI-powered virtual agent. It apologises, explains the disruption and outlines your options. <\/span><\/p>\n

For a moment, that feels like progress. <\/span><\/p>\n

Except nothing actually changes. No ticket is issued. No refund is processed. You close the app knowing what caused the issue and you\u2019re still waiting for it to be resolved. The problem isn\u2019t empathy or intent. The system understood you perfectly. The problem is capability. <\/span><\/p>\n

Many AI-powered interactions are helpful up until something actually needs to be done. <\/span><\/p>\n

This is exactly what Genesys Cloud\u2122 Agentic Virtual Agent<\/a>, powered by large action models (LAMs), was built to address. Our AI agents won\u2019t wait for scripts, prompts or human handoffs. They will be able to recognise what the customer is trying to accomplish, choose the right next steps and keep the work moving across systems without stepping outside established guardrails. <\/span><\/p>\n

The truth is most artificial intelligence (AI) agents in production today cannot carry work through to completion. That limitation rests in the technology underneath the experience, specifically the architectural difference between large language models (LLMs) and large action models: <\/span><\/p>\n

LLMs are designed to understand and explain. LAMs are designed to decide and do. <\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

The Execution Layer of Agentic AI <\/span><\/h2>\n

Large language models transformed how people interact with machines by making conversations feel natural.<\/a> They can understand messy questions, follow context and respond to reflect tone and intent. Within the customer experience (CX), this replaced rigid scripts with dialogue that adapted in real time and is far less mechanical. <\/span><\/p>\n

Trained on vast amounts of text, LLMs excel at predicting language sequences. Interpretation and expression are their defining strengths. <\/span><\/p>\n

They understand what someone is asking and can respond in a way that feels coherent and human. In customer experience, LLMs help make interactions more fluid and informed, but there are limitations to their contributions. <\/span><\/p>\n

That isn\u2019t a flaw. It reflects how LLMs were designed. They reach their ceiling when tasks require workflows that span enterprise systems, policies and time. <\/span><\/p>\n

This is where large action models take over. <\/span><\/p>\n

LAMs extend conversational intelligence into execution. They are built to reason over real-world operations within approved APIs, governed workflows and policy-enforced capabilities<\/a> that already exist inside the organisation. Each operation has defined inputs, permissions and expected outcomes \u2014 giving the model a grounded environment to plan within. <\/span><\/p>\n

That control is intentional. It\u2019s what helps make autonomy safe. <\/span><\/p>\n

Large action models don\u2019t talk about what could happen. They focus on what should happen next and then keep going until the job is complete. Every step is tracked, controlled and easy to follow. <\/span><\/p>\n

This distinction is what sets AI-led platforms apart from AI-assisted systems. Conversations no longer end with \u201csomeone will follow up.\u201d They conclude with completed work and customer resolutions. <\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Introducing the Industry\u2019s First Autonomous Agentic Virtual Agents <\/span><\/h2>\n

The experience you encountered in that airline app isn\u2019t unusual. It\u2019s the expected outcome when conversational systems are asked to perform operational jobs. The virtual agent communicated clearly, yet left the burden on you to wait, retry or escalate. <\/span><\/p>\n

Genesys Cloud Agentic Virtual Agent is designed to take ownership of customer experiences and drive them to completion. These agents will know what they\u2019re allowed to do, how to proceed and when human judgment is needed. <\/span><\/p>\n

Now, let\u2019s revisit the cancelled flight. Instead of listing rebooking options, Genesys Cloud Agentic Virtual Agent can autonomously authenticate you as the traveller, check availability, assign a new seat, apply a credit where policy allows, update the reservation and confirm the result \u2014 all within the same interaction. <\/span><\/p>\n

Now, you leave with certainty, not a recap. <\/span><\/p>\n

This is possible because these agentic virtual agents are powered by the Scaled Cognition APT-1 LAM<\/a>, trained to operate inside real enterprise environments. They will reason against actual tools and policies rather than hypothetical workflows. <\/span><\/p>\n

Every action taken can be traceable and governed. Nothing is improvised or hidden behind a follow-up ticket. <\/span><\/p>\n

Native support for open standards like Agent-to-Agent (A2A) and the Model Context Protocol (MCP) will allow our AI agents to collaborate securely with one another, and across enterprise systems, without losing context or control. <\/span><\/p>\n

At Genesys, execution and governance are inseparable by design. Our agentic virtual agents operate within defined boundaries to enforce policy at runtime and escalate only when human expertise is required. <\/span><\/p>\n

This matters because customers don\u2019t care how many teams or platforms sit behind an interaction. What they want is their issue resolved. Genesys Cloud Agentic Virtual Agent is built to protect that expectation by delivering a single, uninterrupted experience where progress is visible, and outcomes are clear, as work moves across multiple parts of the organisation. <\/span><\/p>\n

Most importantly, the customer experience stops feeling like a series of apologies and explanations and becomes anticipatory through journeys shaped in advance instead of repaired retroactively. <\/span><\/p>\n

If your AI can explain the problem but can\u2019t resolve it, it\u2019s not done yet. With the industry\u2019s first autonomous agentic virtual agents powered by large action models for enterprise CX, Genesys is taking customer experiences beyond conversation into trusted, outcome-driven action at enterprise scale. See it in action Get a demo<\/a> of Genesys Cloud Agentic Virtual Agent today. <\/span><\/p>\n

Genesys Cloud Agentic Virtual Agent powered by large action models is expected to be generally available globally in the first quarter of Genesys fiscal year 2027. <\/span><\/em>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]It\u2019s 8:00 AM and your flight was cancelled overnight. You open the airline app, already bracing for friction. Instead, you\u2019re greeted by a calm, articulate AI-powered virtual agent. It apologises, explains the disruption and outlines your options. For a moment, that feels like progress. Except nothing actually changes. No ticket is issued. No […]<\/p>\n","protected":false},"author":287,"featured_media":613923,"template":"","tax_priority":[54],"tax_blogtype":[17766],"tax_blogcategory":[15928,15977],"tax_contenttheme":[14904],"tax_bundle":[],"tax_contenttheme2":[16139],"tax_capability_sitewide":[16201],"tax_products_programs":[16477,17548],"tax_buying_job":[16651],"tax_buyer_persona":[16887,16919,18552],"tax_sector":[],"tax_segment":[17079,17104],"class_list":["post-614940","blog","type-blog","status-publish","has-post-thumbnail","hentry","tax_priority-54","tax_blogtype-genesys-executives-en-sg","tax_blogcategory-ai-and-machine-learning-en-sg","tax_blogcategory-announcements-en-sg","tax_contenttheme-level-up-your-technology-en-sg","tax_contenttheme2-improve-customer-experience-en-sg","tax_capability_sitewide-ai-and-automation-en-sg","tax_products_programs-genesys-ai-en-sg","tax_products_programs-genesys-cloud-cx-en-sg","tax_buying_job-job-2-solution-exploration-en-sg","tax_buyer_persona-business-en-sg","tax_buyer_persona-technical-en-sg","tax_buyer_persona-technical-en-sg-2","tax_segment-enterprise-en-sg","tax_segment-midsized-en-sg","tax_content_type-blog-en-sg"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/614940","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/users\/287"}],"version-history":[{"count":6,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/614940\/revisions"}],"predecessor-version":[{"id":619070,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/blog\/614940\/revisions\/619070"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/media\/613923"}],"wp:attachment":[{"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/media?parent=614940"}],"wp:term":[{"taxonomy":"tax_priority","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_priority?post=614940"},{"taxonomy":"tax_blogtype","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_blogtype?post=614940"},{"taxonomy":"tax_blogcategory","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_blogcategory?post=614940"},{"taxonomy":"tax_contenttheme","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_contenttheme?post=614940"},{"taxonomy":"tax_bundle","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_bundle?post=614940"},{"taxonomy":"tax_contenttheme2","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_contenttheme2?post=614940"},{"taxonomy":"tax_capability_sitewide","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_capability_sitewide?post=614940"},{"taxonomy":"tax_products_programs","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_products_programs?post=614940"},{"taxonomy":"tax_buying_job","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_buying_job?post=614940"},{"taxonomy":"tax_buyer_persona","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_buyer_persona?post=614940"},{"taxonomy":"tax_sector","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_sector?post=614940"},{"taxonomy":"tax_segment","embeddable":true,"href":"https:\/\/www.genesys.com\/en-sg\/wp-json\/wp\/v2\/tax_segment?post=614940"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}