Part 7/10:

The platform's design ensures that data veracity, quality control, and appropriate agent selection—matching data types to specialized agents—are integral for successful implementations.

Challenges and Critical Success Factors

Scaling agentic AI isn’t without obstacles:

• Data Silos & Quality: Fragmented data hampers effective AI deployment. Building a mature data engine room—with robust metadata, synthetic data, and data governance—is crucial.

• Use Case Selection: Different data and process use cases demand tailored AI models and agents, requiring sophisticated ontology and domain knowledge.

• Talent Shortage: Developing agent engineering skills, combining AI expertise with domain knowledge, remains a bottleneck.