Generative AI agents have strong potential in pharma analytics, but reliable deployment requires more than a conversational interface. Agents need access to domain-specific instructions, approved analytical workflows, validated plotting functions, and internal standards for interpretation and reporting. In this talk, we describe how we introduced a lightweight "Agent Skills" layer into an Ellmer-based agent. We implemented skill selection using tool/function calling with structured inputs. Each skill follows a common agent skill format with a YAML header and body content, giving the agent awareness of available capabilities while minimizing context usage. At runtime, the agent selects a skill name through structured input, and the corresponding full skill is dynamically loaded to guide analysis or execution. These skills are used to instruct the agent to perform analyses consistent with internal standards, call approved plotting functions, and generate structured outputs aligned with team expectations. We will share implementation details, practical lessons, and design considerations for building modular, governed, and extensible AI agents for pharma analytics using ellmer.
Weijie Zhang | Xing Chen
Moderna | Moderna
From Chat to Governed Analysis: Skill-Based AI Agents for Clinical Data Analytics
Generative AI agents have strong potential in pharma analytics, but reliable deployment requires more than a conversational interface. Agents need access to domain-specific instructions, approved analytical workflows, validated plotting functions, and internal standards for interpretation and reporting. In this talk, we describe how we introduced a lightweight "Agent Skills" layer into an Ellmer-based agent. We implemented skill selection using tool/function calling with structured inputs. Each skill follows a common agent skill format with a YAML header and body content, giving the agent awareness of available capabilities while minimizing context usage. At runtime, the agent selects a skill name through structured input, and the corresponding full skill is dynamically loaded to guide analysis or execution. These skills are used to instruct the agent to perform analyses consistent with internal standards, call approved plotting functions, and generate structured outputs aligned with team expectations. We will share implementation details, practical lessons, and design considerations for building modular, governed, and extensible AI agents for pharma analytics using ellmer.