Despite the buzz, agents and agentic AI are not the same.

An agent is a specialist: it retrieves data, summarizes a contract, or checks compliance. It carries memory, applies reasoning, and executes a defined task.

Agentic AI is orchestration. One agent retrieves, another reasons, another validates, and an orchestrator combines the results into a coherent answer. Done right, it acts like a small team that plans, checks, and refines.

The catch? Agentic AI only works if the underlying agents are already strong.

Think in terms of accuracy:

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  If an individual agent delivers ~60% accuracy on its own, chaining several together with explicit validation and error correction can push overall accuracy toward 90%+. In this case, the amplifier effect is very good.

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  If agents fall short of that baseline, the opposite happens. Mistakes are reinforced and amplified. At scale, the system does not just fail quietly, it multiplies error with confidence. That is when the amplifier becomes very expensive.

Now translate that to B2B pricing. Cost, freight, and rebate data are deterministic and must be governed to 99.99%+ accuracy. This is non-negotiable. A 1% cost error on a high-volume SKU cascades through bundles, tiered contracts, and dashboards. It looks precise but leaks margin across every transaction. The amplifier is at work, but it is amplifying the wrong signal.

Other parts of pricing are probabilistic: elasticity models, competitor reactions, customer willingness-to-pay. Here, 60% accuracy can still be useful, because orchestration and validation across agents can surface patterns and improve decisions over time. In this case, the amplifier lifts signal above noise.

Layering on an agentic pricing override workflow does not fix the base error, it scales it. Clean dashboards with flawed inputs are worse than no dashboards at all. Once again, the amplifier does its job, just in the wrong direction.

The path forward is clear:

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  Stabilize workflows with automation — automate cost roll-ups, freight allocations, and rebate reconciliations.

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  Apply AI for probabilistic interpretation — demand forecasting, discount guidance, elasticity sensing.

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  Strengthen individual agents until they are reliably competent in their domain.

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  Govern cost data to 99.99%+ accuracy with audit trails, approval workflows, and exception handling.

Only then does agentic AI deliver its promise. It is not magic. It is an amplifier. With a strong foundation, accuracy compounds. With a weak one, error compounds faster. The outcome is entirely determined by what you choose to amplify.