AI Agent

Warehouse Agent

Warehouse Agent that automates supply chain workflows using your data and tools.

The challenge

Operators walk longer routes than they need to, put stock away wherever there is space, and count the whole warehouse once a year during a shutdown. Variances surface months after the cause, damage disputes come down to memory, and every new starter learns the layout by following someone around.

The outcome

Azure AI Document Intelligence reads the inbound paperwork, a Microsoft Foundry agent plans slotting and picking against the item master held in Azure AI Search, and Azure Functions target counts at the items most likely to be wrong. Stock, task and photo evidence write back to the warehouse system on every movement.

At a glance

Type: ai agents
Category: specialized

Next step

Move from solution to engagement.

Build This Solution

01 — Architecture

End-to-end architecture

Work starts on the floor from a handheld or scanner, flows through a secure API into the task backend, and is planned by the Foundry agent against the item master, the standard operating procedures and live stock. Inbound paperwork is read by Document Intelligence, and every movement is written back to the warehouse management system.

Figure 1 — End-to-end reference architecture for a warehouse operations agent on the Microsoft stack.

02 — Workflow

Process & decision workflow

How stock is received, verified, put away, picked and counted — then branched. Variances inside tolerance confirm automatically and release the next task, while damage or a real discrepancy raises a supervisor investigation with the scan history and photo evidence attached.

Figure 2 — Receive → verify → put away → pick → count → variance branch → confirm or investigate.

03 — Components

Key Microsoft components

Warehouse work is time-critical and evidence-heavy — the agent, its knowledge and its audit trail all stay on the Microsoft stack.

Azure Static Web AppsHandheld and scanner task experience for the floor.
Azure Communication ServicesAlerts, holds and replenishment messages to the team.
Azure API ManagementSecure gateway for WMS, ERP and carrier integrations.
Azure Container AppsTask and agent APIs that scale with shift volume.
Microsoft Foundry Agent ServicePlanning, assignment and verification orchestration.
Azure OpenAI modelsException reasoning and natural language task instructions.
Azure AI SearchGrounding over standard operating procedures and item master.
Azure AI Document IntelligenceReads advance shipping notices, packing lists and labels.
Azure Logic AppsWave building, replenishment triggers and escalations.
Azure FunctionsCycle-count targeting, tolerance rules and metrics.
Azure SQLStock, bin, batch and movement records.
Azure Cosmos DBLive task state, scan events and device sessions.
Azure Key VaultWMS credentials, device certificates and secrets.
Power BI / Microsoft FabricThroughput, accuracy, shrinkage and labour dashboards.

04 — AI

What the agent consumes

The capabilities the agent applies to every task, and the line it does not cross.

AI capabilities embedded in the agent

OCR on labels and shipping notices
Barcode and label reading
Damage image classification
Anomaly detection
Demand-aware slotting
Pick route optimisation
Natural language instructions
Exception reasoning
Cycle-count targeting
Human-in-the-loop routing
Audit summarisation
Workflow orchestration

AI responsibility boundaries

The agent proposes slotting, wave and count decisions, and executes only those inside the configured tolerance. Stock write-offs, damage claims and safety holds always require a named supervisor. Every task retains the scan history, the photo evidence and the rule that was applied.

05 — Personalization

Personalization & evolving process

The same methodology applies to every agent in the catalog. Tune the site, the workflow, the tolerances and the value model — the page structure stays identical.

Site & operation profile

Define the warehouse layout, zones, item master, seasonality and shift patterns. The personas are the picker, the shift supervisor, the inventory controller and the operations planner.

06 — Impact

Key outcomes & business impact

Starting targets for the value case — validate each one against the customer baseline during discovery.

Stock accuracy99%+Continuous, risk-targeted counting instead of an annual freeze.
Pick productivity25%+Shorter travel paths and fewer clarification stops per wave.
Exception handlingMinutesVariances reach a supervisor with the evidence already attached.
Operator onboardingDaysNew starters follow guided tasks instead of memorised routes.