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Classical Industrial AI versus Agentic AI

Kudzai Manditereza

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๐“๐ก๐ž ๐Ž๐ฅ๐ ๐–๐š๐ฒ: ๐‚๐ฅ๐š๐ฌ๐ฌ๐ข๐œ๐š๐ฅ ๐ˆ๐ง๐๐ฎ๐ฌ๐ญ๐ซ๐ข๐š๐ฅ ๐€๐ˆ

โ‡จ A single model, trained on last yearโ€™s data

โ‡จ Hard-coded thresholds that ignore real-world variability

โ‡จ Alerts that say somethingโ€™s wrong, but never tell you why

โ‡จ Hours of manual digging just to understand what happened

โ‡จ Retrain months later โ€ฆ repeat the cycle

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๐“๐ก๐ž ๐๐ž๐ฐ ๐–๐š๐ฒ: ๐€๐ ๐ž๐ง๐ญ๐ข๐œ ๐€๐ˆ

โ‡จ Monitoring Agents: Automatically adjust baselines as conditions evolve.

โ‡จ Domain-SME Agents: Ingest SOPs, manuals, and tribal knowledge to add real-world context.

โ‡จ Correlation & Optimization Agents: Find root causes in real time by analyzing thousands of variables.

โ‡จ Orchestrator Agent: Combines insight, recommended set-points, and confidence into one actionable message.

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Index

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