McKinsey reviewed over fifty agentic AI implementations and found that more than half struggled because they overengineered agents instead of focusing on core workflows first. The high performers created centralized platforms with validated services and reusable components, which reduced redundant development by as much as fifty percent. They also invested in continuous evaluation cycles, integrating domain expertise to flag bias and track decision paths rather than relying on outcomes alone. Results showed that when human oversight was paired with modular design and constant evaluation, adoption and impact were both significantly higher. Source: One year of agentic AI: Six lessons from the people doing the work Follow us (👉@artificialintelligenceee) for everything latest from the AI world.