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SLMs and Agentic AI are redefining enterprise AI strategies—offering more efficient, specialized models and autonomous capabilities that can drive real business value. But with these advancements come new governance challenges. Is your organization prepared to manage the complexity, visibility, and oversight required to scale responsibly?

Join us for an insightful session led by Jim Olsen, CTO of ModelOp, as he breaks down the technical and operational implications of SLMs, model distillation, and Agentic AI architectures. Discover how modern governance strategies can empower your organization to embrace innovation while maintaining control, compliance, and long-term scalability.

What You’ll Learn:

• SLMs vs. LLMs: How Small Language Models provide cost and privacy advantages over traditional LLMs—and why they’re better suited for agentic use cases.
• The Role of Model Distillation: What distinguishes distillation from reinforcement learning, and how tools like DeepSeek and Gemma 3 enable lightweight expert agents.
• Managing Agentic AI Architectures: How to govern large-scale model ensembles and expert models using tools like ModelOp’s Model Inventory and Approval Workflows.
• Operationalizing Governance: How enterprises are applying continuous monitoring, traceability, and structured approvals to stay ahead of compliance and performance risk.

Download the slide deck.

The emergence of small language models (SLMs) is making Agentic AI more accessible and practical for enterprises. Unlike large language models that require massive computational resources and expensive GPUs, SLMs can run on standard desktop hardware with just 4GB of GPU memory. This shift enables organizations to deploy specialized AI agents locally without sending sensitive data to external vendors, making Agentic AI cost-effective and secure for enterprise use.