Thomson Reuters' Chief Product Officer has outlined four essential pillars for building successful "agentic AI" systems in professional settings, revealing that many organizations are currently grappling with integrating these crucial elements effectively. Agentic AI, which refers to AI systems capable of independent action and decision-making to achieve specific goals, holds immense potential for transforming industries, but realizing this potential requires a holistic approach.

Speaking at a recent industry conference, David Colangelo, CPO of Thomson Reuters, emphasized the interconnectedness of these pillars, arguing that neglecting even one can significantly hinder the performance and reliability of agentic AI. He identified the first pillar as **Robust Data Foundations**. Colangelo stressed that agentic AI thrives on high-quality, accurate, and comprehensive data. Without a solid foundation of reliable information, these systems are prone to errors, biases, and ultimately, flawed decision-making. He noted that many companies struggle with data silos, inconsistent data formats, and a lack of proper data governance, making it difficult to train and deploy effective agentic AI.

The second pillar is **Deep Domain Expertise**. While AI can process vast amounts of information, it lacks the contextual understanding and nuanced judgment of human experts. Colangelo explained that successful agentic AI must be infused with deep domain knowledge to interpret data accurately and make informed decisions within specific industries or professions. This requires close collaboration between AI developers and subject matter experts.

**Explainable AI (XAI)** forms the third critical pillar. Colangelo argued that transparency is paramount, especially in professional settings where decisions have significant consequences. Users need to understand how an agentic AI system arrived at a particular conclusion to build trust and ensure accountability. XAI techniques allow users to trace the reasoning process of the AI, identifying the factors that influenced its decisions.

Finally, the fourth pillar is **Human-in-the-Loop Integration**. Colangelo cautioned against fully autonomous AI systems, particularly in complex professional contexts. He advocated for a human-in-the-loop approach, where humans retain oversight and control, intervening when necessary to correct errors, provide guidance, or handle unforeseen circumstances. This ensures that agentic AI augments human capabilities rather than replacing them entirely.

Colangelo's presentation highlighted the challenges companies face in bringing these four pillars together. Building agentic AI is not simply about deploying sophisticated algorithms; it requires a strategic and integrated approach that addresses data quality, domain expertise, explainability, and human oversight. The companies that can successfully navigate these