AI agents for supply chain and procurement are purpose-built, autonomous software components that carry out tasks such as real‑time visibility, rate optimization, supplier selection, contract analysis and exception resolution. The topic spans marketplaces that host reusable agents, agent frameworks and infrastructure for orchestration, no‑/low‑code platforms for business teams, and specialized autonomous logistics tools for carriers and freight forwarders. This space is increasingly relevant in 2026 as freight markets remain volatile, customers demand end‑to‑end transparency, and enterprises look to scale automation without rewriting legacy integrations. Platforms and tools mentioned here illustrate the range of approaches: CargoBrain targets air‑cargo workflows and pricing optimization for airlines and forwarders; Tektonic AI blends neural and symbolic reasoning to automate revenue‑sensitive enterprise processes; Relevance AI offers a no‑code/low‑code environment to build and manage multi‑agent systems; Xilos positions itself as an observable, agentic infrastructure; Yellow.ai focuses on customer and employee‑facing autonomous agents; and Claude family models and developer assistants (and developer tools like Amazon CodeWhisperer/Amazon Q) provide conversational, analytical and coding support to accelerate agent development. Notion and similar workspaces are often used to combine knowledge, automation and integrations for operational workflows. Common use cases include dynamic pricing and capacity matching (freight AI), procurement automation and sourcing orchestration (Oracle agents and enterprise automation), and multimodal exception management and visibility (project44‑style visibility paired with agentic orchestration). Key trends are multi‑agent orchestration, hybrid neural+symbolic reasoning, increased emphasis on observability and governance, and broader adoption of low‑code marketplaces — tempered by ongoing challenges around data quality, integration complexity, explainability and compliance.