AI Agents Skilled in Supply Chain Commerce

Machine learning finds patterns in data and supports predictive analytics such as demand forecasting. Generative AI creates or summarizes information in natural language. Agentic AI combines reasoning, workflows, tools, and approved actions so an AI agent can work through a task across multiple steps. ActiveAgents apply that model to specific supply chain and commerce jobs.

Yes. ActiveAgents use large language models (LLMs) as part of their reasoning layer, with natural language processing so users can ask questions in plain language. Their responses are grounded in the real-time data, workflows, and domain rules for the assigned job, while approved actions are executed through governed tools and APIs.

No. ActiveAgents are not a replacement for your ERP or supply chain management system. They are AI capabilities that work within your operational applications, using the data, business rules, and APIs already governing your processes.

ActiveAgents help teams understand what is happening, identify the right action, and execute approved work, while your ERP or supply chain system remains the system of record. In Manhattan’s architecture, agents operate across solutions such as warehouse management (WMS), transportation management (TMS), order, store, and planning applications, with controls that respect system-of-record boundaries.

ActiveAgents use retrieval-augmented generation (RAG) and grounding to reference relevant enterprise and operational data, not just a model’s general knowledge. Their responses are grounded in the real-time context, workflows, and business rules of the assigned operation, while governed APIs and execution controls help ensure that approved actions follow those rules.

Teams establish a baseline, then compare workflow and business outcomes after deployment. Metrics may include time saved, tickets deflected, transactions automated, cycle time, fill rate, labor utilization, exception-handling time, and working-capital impact. Execution tracing and structured logging show what the agent did and help connect its actions to measurable results.

Agents can take certain approved autonomous actions. Other actions keep a human-in-the-loop for approval based on the workflow, permissions, and business policies. In either case, actions are bounded by governed APIs and business rules and can be monitored through execution tracing.

ActiveAgents can work in multi-agent systems, collaborating with other agents and tools using supported protocols such as A2A and MCP. They can also use data made available through supported tools and integrations. Specific IoT or digital twin connectivity depends on the data source and implementation.

ActiveAgents can help identify and resolve inventory-related exceptions such as wave shortages by tracing root cause and surfacing available alternatives. Support for excess inventory depends on the planning workflow and the agents configured for that operation.

If a disruption falls outside an agent’s configured workflow or guardrails, it should surface the relevant context for human review rather than take an unauthorized action. Teams can then investigate, adjust the workflow, or extend the agent for that scenario using Agent Foundry™.