From Pilots to Real Impact

Creating effective AI agents goes far beyond writing prompts — it’s about making the right configurations, choosing the right tools, and ensuring they can scale from testing to production. In this session, we’ll explore how builders can create, fine-tune, and test agents within Glob.AI OS, experimenting with tool integrations and configurations to solve real-world business challenges. You’ll see practical use cases, learn proven best practices for iteration and testing, and understand how to move confidently from prototype to production. This is the essential deep dive for anyone looking to build enterprise-ready agents. You’ll leave with actionable insights to build AI agents that perform reliably, scale efficiently, and deliver measurable business impact.

Developing new oncology treatments is a race against time — one defined by long timelines, high costs, and massive data complexity.

In this session, we’ll explore how PharmaMar, a global biopharmaceutical leader in cancer research, partnered with Globant to transform its R&D decision-making through an agentic AI approach.

You’ll discover how this collaboration enables scientists to access and connect decades of preclinical and clinical knowledge with real-time external data — accelerating insights, improving accuracy, and reducing uncertainty across research phases.

We’ll explore Glob.AI OS’s role in enabling this transformation, providing the multi-agent framework that powers PharmaMar’s “co-scientist” system — designed to integrate, validate, and operationalize knowledge across the entire R&D cycle.

From discovery to clinical design, you’ll leave with an inside look at how AI is helping PharmaMar bring life-saving treatments to patients faster — and how human expertise and AI can work together to unlock smarter, faster, and safer R&D decisions.

Managing AI at scale requires more than deploying models — it demands visibility, governance, and cost control. In this session, we’ll explore how Glob.AI OS enables organizations to monitor LLM usage in real time, set consumption limits across projects or teams, and keep costs predictable. By the end of this session, you’ll have a clear understanding of how transparent dashboards and quota management provide the foundation for sustainable AI adoption across the enterprise.