{"id":599194,"date":"2025-10-16T17:01:51","date_gmt":"2025-10-17T00:01:51","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/how-genesys-and-scaled-cognition-are-shaping-the-future-of-agentic-orchestration"},"modified":"2025-10-16T17:02:56","modified_gmt":"2025-10-17T00:02:56","slug":"how-genesys-and-scaled-cognition-are-shaping-the-future-of-agentic-orchestration","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-sg\/blog\/post\/how-genesys-and-scaled-cognition-are-shaping-the-future-of-agentic-orchestration","title":{"rendered":"How Genesys and Scaled Cognition Are Shaping the Future of Agentic Orchestration"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]Consumer loyalty is tested in the moments that matter most. A customer spots an unauthorised charge and wants it resolved on the spot; a policyholder calls after a fire desperate for guidance while their home still smoulders; or a patient waits for treatment approval with each day of delay carrying real consequences. These are not times for apologies or vague promises. People expect progress and empathy delivered as swift, dependable action from both artificial intelligence (AI) and human agents.<\/span><\/p>\n

Genesys is focused on meeting this standard with agentic orchestration. Today we announced a new partnership with Scaled Cognition that will deepen our capabilities to build AI that turns intent into outcomes. <\/span><\/p>\n

Together, we\u2019re charting the future where AI agents don\u2019t stop at conversations. They will take the next action, resolve the issue, and deliver results reliably and under enterprise-grade governance. By integrating the Scaled Cognition APT-1 large action model into the Genesys Cloud\u2122 platform<\/a>, organisations will be able to deploy autonomous virtual agents that can complete workflows in real time, adhere to compliance policies and deliver customer experiences where resolutions define success.<\/span><\/p>\n

This is the next evolution of agentic orchestration<\/a> \u2014 AI systems built to carry intent through to completion by turning complex workflows into predictable, repeatable outcomes.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Turning Words Into Actions with LAMs\u00a0<\/span><\/h2>\n

Traditional large language models (LLMs) are trained from text and designed to converse about next steps rather than execute them. They can explain how a claim will be processed or when an account might be updated, but they can\u2019t reliably perform the outcome.\u00a0\u00a0\u00a0<\/span><\/p>\n

Large action models (LAMs) extend the conversational strengths of LLMs by adding the ability to decide, act and verify. This will enable richer, more complex experiences and empower virtual agents to carry a workflow to completion across multiple systems and policies.\u00a0\u00a0<\/span><\/p>\n

LAMs are trained to take actions by combining conversation with the policies and rules that govern real-world workflows. Instead of just predicting the next word, they also decide on the correct step, call the right API, enforce the right policy or escalate when needed.\u00a0\u00a0\u00a0<\/span><\/p>\n

Because execution is part of their training, they can behave more deterministically, reduce variance and deliver more reliable outcomes. The impact will be stronger containment, faster resolution and the ability to eliminate hallucinations. Businesses gain efficiency and compliance assurance while customers see faster and better results.\u00a0<\/span><\/p>\n

Consider a billing-resolution scenario: A customer disputes a charge and reaches out for help. An LLM-based agent responds with empathy, guides the customer through the process and outlines what will happen next.\u00a0 The actual refund, however, relies on downstream processes and follow-up steps.\u00a0\u00a0<\/span><\/p>\n

A LAM-based agent would expand these capabilities by authenticating the account, posting the credit, verifying the transaction and confirming the resolution within the same interaction. In this case, a solution could be delivered immediately. It closes the loop for the customer and executes the downstream operational steps all within the same interaction.\u00a0<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

The Genesys and Scaled Cognition Advantage <\/span><\/h2>\n

Anyone can license a traditional LLM. Beyond access to models, what makes Genesys different is how we orchestrate them inside our unified platform that\u2019s purpose-built for customer experience (CX). <\/span>
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\nGenesys Cloud already supports a broad range of options, including Genesys-built, open-source and frontier models through Amazon Bedrock. This allows enterprises to innovate with models that best align with their goals while maintaining safety, control and performance. <\/span><\/p>\n

Scaled Cognition LAMs build on that foundation. They combine the conversational strength of LLMs with action-driven intelligence. By pairing the orchestration power of Genesys Cloud with the Scaled Cognition purpose-built models for customer experience, enterprises will have access to autonomous agents that can operate with even more precision and control. <\/span><\/p>\n

Organisations will be able to design virtual agents that can reliably execute complex tasks, coordinate seamlessly with human teams and uphold compliance standards. What makes this distinctive is how intricate business logic is translated into deterministic behaviours to produce outcomes that are consistent, auditable and aligned with enterprise policies, including: <\/span><\/p>\n