Agentic AI marks a shift from single, predictive models to coordinated systems of autonomous agents that execute complex workflows across enterprise environments. Success depends on more than model size—it requires structured knowledge, robust orchestration, memory management, grounding in trusted data, observability, and modular integrations. Without this foundation, organizations risk amplifying errors at scale, creating costly inefficiencies and compliance issues. Enterprises should pilot low-risk, high-value use cases, fix knowledge architecture, embed governance from day one, and align AI initiatives with measurable business outcomes to scale safely and effectively.

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