Focus Areas:

Organizational model for centralized vs. federated agent development
Reusable component library approach - build once, deploy everywhere
Quality assurance frameworks for AI agents - testing what can't be deterministic
Change management strategies - getting adoption beyond the tech enthusiasts
Metrics that matter - measuring agent effectiveness and business impact

Key Takeaways:

Blueprint for standing up an agent development CoE (Center of Excellence)
Practical templates for agent lifecycle management
Framework for choosing build vs. buy vs. hybrid approaches
Real cost models from production deployments
Governance model balancing innovation speed with risk management