Deep Research Agents for Supply Chain

Deep research agents represent a rapidly emerging frontier in supply chain AI, where large language models (LLMs) are coupled with planning, tool use, and retrieval capabilities to conduct sophisticated, multi-step research tasks autonomously. Unlike traditional question-answering systems, deep research agents can decompose complex queries—such as "assess the geopolitical exposure of our tier-2 semiconductor suppliers"—into sub-tasks, gather evidence from heterogeneous sources, synthesise findings, and produce structured, citation-backed reports.

In the supply chain context, deep research agents are particularly valuable for tasks that have traditionally required extensive human analyst effort: supplier due diligence, regulatory compliance monitoring, ESG and sustainability auditing, competitor intelligence, and risk scanning across multi-tier networks. By orchestrating web search, document analysis, database queries, and structured reasoning, these agents can dramatically compress the time needed to produce actionable intelligence—while maintaining traceability through explicit citation of evidence.

Research in this area spans foundational work on LLM-based agent architectures (such as ReAct, reflection, and plan-and-solve paradigms), retrieval-augmented generation (RAG), tool-use frameworks, and domain-specific adaptations for supply chain knowledge. Contributions from the Supply Chain AI Lab at the University of Cambridge, alongside broader advances in agentic AI from OpenAI, Anthropic, and Google DeepMind, are shaping the methodology and benchmarks for this emerging field.

We invite you to explore the curated collection of key publications below, offering a gateway into this fast-moving area of research.

List of Publications

1. Xu, L., Almahri, S., Mak, S. and Brintrup, A., 2024. Multi-agent systems and foundation models enable autonomous supply chains: Opportunities and challenges. IFAC-PapersOnLine, 58(19), pp.795-800. [PDF]
2. Jannelli, V., Schoepf, S., Bickel, M., Netland, T. and Brintrup, A., 2025. Agentic LLMs in the supply chain: Towards autonomous multi-agent consensus-seeking. International Journal of Production Research (online). [PDF]
3. Zheng, G., Almahri, S., Xu, L., Minaricova, M. and Brintrup, A., 2025. LLMs in supply chain management: Opportunities and a case study. IFAC-PapersOnLine. [PDF]
4. Almahri, S., Xu, L. and Brintrup, A., 2025. Enhancing supply chain visibility with knowledge graphs and large language models. International Journal of Production Research (online). [PDF]
5. Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G. and MacCarthy, B.L., 2024. Digital supply chain surveillance using artificial intelligence: Definitions, opportunities and risks. International Journal of Production Research, 62(13), pp.4674-4695. [PDF]