Manhattan Associates has introduced Agentic AI capabilities across its Manhattan Active suite, empowering users to deploy autonomous digital agents built on large language models and cloud-native architecture. The launch includes a new Agent Foundry platform that enables customers to design their own task-specific agents to drive operational efficiency and responsiveness across logistics and retail fulfillment workflows.
Addressing Supply Chain Friction Points
With rising complexity in supply chain operations, from labor planning to omnichannel fulfillment, Manhattan Associates is aiming to eliminate system lag and interface friction through its Agentic AI initiative. The company’s new suite of digital agents can interpret natural language requests, dynamically manage tasks, and reconfigure operations on the fly, offering a layer of intelligent responsiveness traditionally missing in enterprise platforms.
Initial AI agents include the Intelligent Store Manager, Labor Optimizer Agent, Wave Inventory Research Agent, Contextual Data Assistant, and Virtual Configuration Consultant. These tools are engineered to help logistics professionals interact with systems conversationally, automate routine decisions, and expedite access to operational data across warehouse, store, and transportation environments.
By offloading repetitive tasks and facilitating decision support in real time, the agents aim to close latency gaps in supply chain execution, something especially critical in high-volume environments where a single delay can ripple through downstream operations.
Customization Without Waiting on Vendors
At the heart of the rollout is Manhattan Agent Foundry, a developer-friendly environment where customers and partners can create their own agents tailored to unique business needs. By decoupling development from Manhattan’s product roadmap, the company is effectively giving its users direct control over innovation cadence.
This approach supports industry-standard interoperability via A2A and MCP protocols, meaning businesses can integrate third-party platforms and orchestrate multi-agent collaboration across complex supply chain ecosystems. The aim: shorten the path from data insight to action.
For logistics teams working across fragmented systems, this architecture could be a turning point. Instead of submitting tickets or relying on IT intermediaries, business users can now generate actionable workflows, accelerate root cause analysis, and adapt to disruptions with greater autonomy.
Configurability May Outweigh Autonomy
Manhattan’s agentic approach is well-aligned with industry priorities—speed, flexibility, and reduced reliance on static interfaces. But while autonomous operation may attract headlines, the more enduring value for many organizations may lie in the configurability and interoperability of these agents. As agent-based orchestration becomes more common, the success of such initiatives will depend on governance, training, and the ability to manage these agents over time. It’s a promising development, but one that must be paired with clear operational accountability to ensure that autonomy enhances, not obscures, performance visibility.
