Table of contents
See how life sciences leaders are designing governed AI agents to perform work throughout the end-to-end supply network.
At LogiPharma Europe 2026, TraceLink hosted an executive masterclass where supply chain leaders designed custom AI agents for their own operations. In just 15 minutes, participants moved from theory to practice—creating a spectrum of use cases that demonstrate how governed AI operates as a digital teammate within a multienterprise network.
Executing Tasks vs. Performing Work
To operationalize AI, organizations must distinguish between two levels of automation:
- Executing Tasks: The automation of discrete, rule-based steps such as data entry or status updates.
- Performing Work: The orchestration of complex processes where agents evaluate context, apply governance rules, and execute outcomes to achieve a business objective.
A network-native architecture allows these agents to perform work and execute tasks within pre-defined governance rules.
This summary report captures how users can create these agents using plain text prompts, highlighting the real-world agent designs and survey insights from your peers who participated in the LogiPharma masterclass.
What You’ll Discover
- Agent “Job Profiles”: How to define the intents, objectives, and decisions for agents across the value chain.
- Policy-Driven Execution: Shifting from reactive exception management to proactive, autonomous orchestration.
- Measurable Productivity Gains: How to achieve gains in agility and performance using a network-native data foundation and strict governance guardrails.
Download the Masterclass Summary to explore the agentic use cases and discover how governed AI can begin executing tasks and performing work across your network.
