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Beyond LLMs: Building an AI-Powered Decision System at Scale
Originally aired:
About the Session
The explosion of Generative AI (GenAI) has brought rapid adoption across industries. However, statistics show that over 50% of GenAI models fail to transition from pilot to production. While current implementations focus on chatbots, sentiment analysis, and document extraction, organizations are now pushing the boundaries—demanding complex, decision-driven AI workflows beyond basic conversational capabilities.
This session will explore Agentic AI platforms, which integrate Large Language Models (LLMs) and Large Action Models (LAMs) to execute reasoning and decision-making workflows at scale. Through case studies, we’ll uncover how frameworks like ReAct (Reasoning + Action) enable AI-driven decision-making beyond simple query-response tasks.
We’ll also discuss practical strategies for building transparent, scalable, and governable AI systems, utilizing open-source frameworks, cloud-native tools, and AI governance models to ensure reliability, accountability, and compliance.
Key Takeaways
- Beyond Conversational AI – How Agentic AI extends beyond chatbots to power decision-driven workflows.
- ReAct Framework – Implementing reasoning and action models for intelligent automation.
- Scalable & Governable AI – Strategies for deployment, monitoring, and ensuring AI transparency.
- Enterprise-Ready AI – Leveraging cloud-native tooling and open-source libraries for AI at scale.
Target Audience
- AI Architects & ML Engineers – Designing decision-based AI systems beyond LLM interactions.
- Enterprise AI Leaders & Innovation Teams – Implementing scalable AI for business transformation.
- AI Governance & Compliance Professionals – Ensuring responsible, transparent, and compliant AI systems.
Join us to discover how Agentic AI, LLMs, and LAMs are redefining the future of intelligent, scalable, and decision-driven AI architectures.
Beyond LLMs: Building an AI-Powered Decision System at Scale
Originally aired:
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