Manhattan Associates today rolled out a suite of updates to its Manhattan Active® Omni platform designed to boost both in-store and online sales for retailers. Key additions include three embedded AI agents — a Store Associate Agent, a Contact Center Agent and an OMS Configuration Agent — that reside natively within the user interface and leverage natural language queries to deliver real-time insights on store traffic, sales trends, inventory levels, returns and customer behavior. The company also expanded its Point of Sale offering with a Customer Facing Display that allows shoppers to view their cart, attach loyalty information, enter shipping details and select receipt options in real time, driving faster checkout times and higher accuracy. Complementing these front-end enhancements, Manhattan introduced its Fulfillment Optimization Simulation engine, which lets retailers model multiple fulfillment strategies using consistent order datasets. Users can compare trade-offs in cost, speed, service level and margin side-by-side, adjust optimization rules, rerun simulations and conduct “what-if” scenario planning to uncover savings and improve service levels before implementing changes in live operations.

In parallel with its Omni enhancements, Manhattan Associates announced the commercial launch of its AI Agent Workforce, a generation of intelligent agents embedded across all Manhattan Active® solutions. Interactive Agents act as digital assistants for roles such as store associates and contact center reps, while Autonomous Agents continuously monitor operations, automate repetitive tasks and remediate issues without human intervention. Examples include a Labor Agent that optimizes workforce deployment based on pending work, a Shipment Tracking Agent that flags potential delays and recommends corrective actions, and a Wave Coordinator Agent used by Eaton to accelerate fulfillment workflows in its warehouse network. Customers also gain access to Manhattan Agent Foundry™, a platform enabling rapid creation and customization of new agents via natural language or APIs, all compliant with A2A and MCP standards to ensure seamless interoperability. Early adopters report measurable uplifts in operational efficiency, with labor planning recommendations driving double-digit productivity gains and root-cause diagnostics reducing escalation rates by over 20%.