Why agentics projects fail and how to fix them

Agentic AI projects can produce substantial operational gains, such as automating support and order-processing tasks, but many fail because organizations lack accurate, governed, and accessible data rather than because their underlying models are inadequate. The text identifies operational correctness, control-plane, and human-supervision risks, arguing that agents can amplify errors when they act automatically on stale data, insufficient context, weak permissions, or poorly auditable systems. Successful deployments require centralized and modeled data, lineage, semantic definitions, governance, and interoperability so agents can use authoritative information consistently across systems. Appropriate use cases are high-volume, structured, text- or code-heavy workflows with clear outcomes, low-cost review, and reversible actions, while ambiguous, high-stakes, adversarial, or irreversible work is less suitable. Rather than building models from scratch, organizations can augment foundation models with proprietary data through retrieval-augmented generation and introduce autonomy gradually, beginning with read-only retrieval, then human-reviewed drafting, and finally tightly bounded write actions with approvals for higher-risk decisions.

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