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Replenishment Intelligence: Agent2Agent

What is Agent2Agent?​

Agent2Agent (A2A) is how the specialist agents behind Replenishment Intelligence work together so the business user does not have to stitch the analysis by hand.

Behind the scenes, replenishment is not one model - it is a team of agents:

  • a Forecasting agent that produces Demand Signals,
  • an Anomaly agent that produces Store Signals,
  • a PO agent that turns those into recommendations, and
  • a Report agent that summarizes the outcome.

Agent2Agent is the collaboration layer that lets these agents hand work to one another and assemble a single, coherent answer.

The reorder loop​

The core A2A flow is the reorder loop. A business user asks, in plain language:

"What should I reorder?"

and the agents collaborate to answer:

  1. Forecast - the Forecasting agent establishes demand over the planning horizon.
  2. Anomaly - the Anomaly agent overlays store-level risk on that demand.
  3. PO - the PO agent reconciles demand, inventory position, and risk into recommended quantities.
  4. Report - the result is summarized with its decision traces, ready to review and act on.

The user gets a defensible plan back without manually running forecasts, pulling anomaly reports, and reconciling spreadsheets.

Self-awakening on fresh data​

A2A also means the system reacts to new data rather than waiting to be asked. When the underlying sales and inventory data refreshes:

  • the relevant signals are recomputed, and
  • the replenishment view reflects the latest picture.

The user opens the module to a current plan instead of stale numbers.

The Decision Command Center​

As agents collaborate, their activity is surfaced rather than hidden. The user can see the chain of work - which signal was produced, which risk was found, which recommendation resulted - so the collaboration is transparent, not magic.

This same transparency produces a decision trace on every recommendation: the demand input, the inventory position, the store-level risks that adjusted the quantity, and the rule path that led to the final suggested units. Because the agents pass structured signals to one another, the reasoning is preserved end to end - so the user can always answer "why this number?" and defend it to leadership or the retailer.

How the business user experiences it​

  • Ask in plain language - a conversational panel inside the Replenishment Intelligence Center answers reorder and risk questions against the live data.
  • One coherent answer - demand, risk, and recommended action arrive together, already reconciled.
  • Always defensible - because the agents pass structured signals to one another, the reasoning is preserved end to end and shown as a decision trace.

Why it matters for business​

  • No manual stitching - one question replaces a multi-tool, multi-spreadsheet workflow.
  • Faster decisions - the loop from "what should I reorder?" to a defensible plan is minutes, not days.
  • Consistency - every user gets the same rigorous flow, every time, with the reasoning attached.

See PO Recommendations for how the recommended quantity is built, and Overview for how the three signal layers fit together.