Having autonomous agents that solve complex problems and can make decisions in unexpected situations is promising. That\u2019s the reason why most businesses across various industries are currently exploring Agentic AI.<\/p>\n
This tutorial presents a research-based, practical guide for designing enterprise-ready multi-agent systems based on our experience in building our Agentic Platform.<\/p>\n
We will describe design principles derived from theoretical concepts and research papers. First, agents and their basic architecture and capabilities will be introduced. Their capabilities for long-term, complex tasks motivate their use in various industries such as supply chain management or finance. Second, agentic patterns, frameworks, and emerging communication protocols will be discussed. Third, we will cover different types of short-term and long-term memory and how to utilize them for agents to follow business rules. Once we have a basic agent design, we need to consider the observability of agents and techniques for online\/offline evaluations. Finally, we will discuss trustworthy AI, including the implementation of guardrails, fact-checking, and alignment of objectives.<\/p>\n
Attendees will learn the design principles for secure, reliable, and production-ready agent systems that reflect the current state of the art in research.<\/p>\n
This tutorial is aimed at AI\/ML engineers, Software Architects, and AI researchers who are interested in understanding agentic architecture and how the latest research applies in enterprise applications.<\/p>\n
\n- Basic understanding of how an LLM works and basic knowledge about current architectures.<\/li>\n
- Fundamental programming skills in any language, but the code shown will be Python.<\/li>\n
- Some principles of composable software architecture.<\/li>\n<\/ul>\n
<\/p>\n
Outline and Description of the Tutorial<\/p>\n
\n- Introduction to agents: Defining agentic systems and the motivation to use them in the Enterprise. (20 min)<\/li>\n
- Agentic patterns, frameworks and protocols: Explores common agentic patterns (ReACT, Reflection, CoT). Overview of the popular frameworks (SmolAgents, LangGraph, LlamaIndex, CrewAI). Emerging communication protocols: A2A, MCP. Their benefits and concerns. (40 min)<\/li>\n
- Agentic memory: Defining a multi-layer memory structure: short-term for objectives, long-term for business alignment. Emerging tools for semantic caching. (20 min)<\/li>\n
- Observability and evaluation: Introduction to agent observability, motivation for online and offline evaluation. Emerging tools to deal with agentic observability. (20 min)<\/li>\n
- Trustworthy AI: Implementation of safety layers: Guardrails, Fact Checks, and objective alignment. (20 min)<\/li>\n<\/ul>\n
<\/p>\n
Reading List<\/p>\n
Introductory papers: These papers provide an overview of the agentic AI topic, consisting of surveys on some of the topics mentioned in the tutorial:<\/p>\n
\n- Luo, Junyu, Weizhi Zhang, Ye Yuan, et al. \u201cLarge Language Model Agent: A Survey on Methodology, Applications and Challenges.\u201d arXiv:2503.21460. Preprint, arXiv, March 27, 2025. https:\/\/doi.org\/10.48550\/arXiv.2503.21460<\/a>.<\/li>\n
- Du, Shangheng, Jiabao Zhao, Jinxin Shi, et al. \u201cA Survey on the Optimization of Large Language Model-Based Agents.\u201d arXiv:2503.12434. Preprint, arXiv, March 16, 2025. https:\/\/doi.org\/10.48550\/arXiv.2503.12434<\/a>.<\/li>\n
- Yang, Yingxuan, Huacan Chai, Yuanyi Song, et al. \u201cA Survey of AI Agent Protocols.\u201d arXiv:2504.16736. Preprint, arXiv, June 21, 2025. https:\/\/doi.org\/10.48550\/arXiv.2504.16736<\/a>.<\/li>\n
- Pradhan, Anu, Alexandra Ortan, Apurv Verma, and Madhavan Seshadri. \u201cLLM-as-a-Judge: Rapid Evaluation of Legal Document Recommendation for Retrieval-Augmented Generation.\u201d arXiv:2509.12382. Preprint, arXiv, September 15, 2025. https:\/\/doi.org\/10.48550\/arXiv.2509.12382<\/a>.<\/li>\n
- Yehudai, Asaf, Lilach Eden, Alan Li, et al. \u201cSurvey on Evaluation of LLM-Based Agents.\u201d arXiv:2503.16416. Preprint, arXiv, March 20, 2025. https:\/\/doi.org\/10.48550\/arXiv.2503.16416<\/a>.<\/li>\n<\/ul>\n
There are short courses from Hugging Face that can help the attendees get hands-on practice:<\/p>\n
\n- https:\/\/huggingface.co\/agents-course<\/a>: Agents course: Covers from a developer
\nperspective the most popular frameworks: SmolAgents, LangGraph, and LlamaIndex.<\/li>\n- https:\/\/huggingface.co\/mcp-course<\/a>: MCP course: The popular model context protocol
\nis gaining traction to allow agents to use tools and act in the real world.<\/li>\n- https:\/\/huggingface.co\/learn\/llm-course\/<\/a>: Fine-tuning Language Models: Covers topics
\nfrom Instruction tuning, preference alignment, and reinforcement learning.<\/li>\n<\/ul>\nBooks<\/strong><\/p>\nThe following books from Chip Huyen provide an overview of building generative AI applications (\u201cAI engineering: building applications with foundation models\u201d) and \u201cDesigning Machine Learning Systems\u201d for an overview of classical ML. Her blog is also a valuable source of information: https:\/\/huyenchip.com\/blog\/<\/a><\/p>\nThe following reading list includes and extends the topics that will be covered during the tutorial.<\/p>\n
Foundation models, architecture, and problems:<\/strong><\/p>\n\n- Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. \u201cAttention Is All You Need.\u201d
\narXiv:1706.03762. Preprint, August 2, 2023. https:\/\/doi.org\/10.48550\/arXiv.1706.03762<\/a>.<\/li>\n- Zhang, Muru, Ofir Press, William Merrill, Alisa Liu, and Noah A. Smith. \u201cHow Language Model
\nHallucinations Can Snowball.\u201d arXiv:2305.13534. Preprint, May 22, 2023.
\nhttps:\/\/doi.org\/10.48550\/arXiv.2305.13534<\/a>.<\/li>\n- Ouyang, Long, Jeff Wu, Xu Jiang, et al. \u201cTraining Language Models to Follow Instructions with
\nHuman Feedback.\u201d arXiv:2203.02155. Preprint, March 4, 2022.
\nhttps:\/\/doi.org\/10.48550\/arXiv.2203.02155<\/a>.<\/li>\n- Villalobos, Pablo, Anson Ho, Jaime Sevilla, Tamay Besiroglu, Lennart Heim, and Marius
\nHobbhahn. \u201cWill We Run out of Data? Limits of LLM Scaling Based on Human-Generated
\nData.\u201d arXiv:2211.04325. Preprint, June 4, 2024. https:\/\/doi.org\/10.48550\/arXiv.2211.04325<\/a><\/li>\n<\/ul>\nEvaluation methodologies:<\/strong><\/p>\n\n- Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. \u201cEfficient Estimation of Word Representations in Vector Space.\u201d arXiv:1301.3781. Preprint, September 7, 2013. https:\/\/doi.org\/10.48550\/arXiv.1301.3781<\/a>.<\/li>\n
- Gehrmann, Sebastian, Abhik Bhattacharjee, Abinaya Mahendiran, et al. \u201cGEMv2: Multilingual NLG Benchmarking in a Single Line of Code.\u201d arXiv:2206.11249. Preprint, June 24, 2022. https:\/\/doi.org\/10.48550\/arXiv.2206.11249<\/a>.<\/li>\n
- Wang, Alex, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. \u201cGLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding.\u201d arXiv:1804.07461. Preprint, February 22, 2019. https:\/\/doi.org\/10.48550\/arXiv.1804.07461<\/a>.<\/li>\n
- Wang, Yubo, Xueguang Ma, Ge Zhang, et al. \u201cMMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.\u201d arXiv:2406.01574. Preprint, November 6, 2024. https:\/\/doi.org\/10.48550\/arXiv.2406.01574<\/a>.<\/li>\n
- Muennighoff, Niklas, Nouamane Tazi, Lo\u00efc Magne, and Nils Reimers. \u201cMTEB: Massive Text Embedding Benchmark.\u201d arXiv:2210.07316. Preprint, March 19, 2023. https:\/\/doi.org\/10.48550\/arXiv.2210.07316<\/a>.<\/li>\n
- Wang, Alex, Yada Pruksachatkun, Nikita Nangia, et al. \u201cSuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems.\u201d arXiv:1905.00537. Preprint, February 13, 2020. https:\/\/doi.org\/10.48550\/arXiv.1905.00537<\/a>.<\/li>\n<\/ul>\n
AI as a Judge:<\/strong><\/p>\n\n- Zhu, Lianghui, Xinggang Wang, and Xinlong Wang. \u201cJudgeLM: Fine-Tuned Large Language
\nModels Are Scalable Judges.\u201d arXiv:2310.17631. Preprint, March 1, 2025.
\nhttps:\/\/doi.org\/10.48550\/arXiv.2310.17631<\/a>.<\/li>\n- Zheng, Lianmin, Wei-Lin Chiang, Ying Sheng, et al. \u201cJudging LLM-as-a-Judge with MT-Bench andChatbot Arena.\u201d arXiv:2306.05685. Preprint, December 24, 2023. https:\/\/doi.org\/10.48550\/arXiv.2306.05685<\/a>.<\/li>\n
- Valmeekam, Karthik, Matthew Marquez, and Subbarao Kambhampati. \u201cCan Large Language Models Really Improve by Self-Critiquing Their Own Plans?\u201d arXiv:2310.08118. Preprint, October 12, 2023. https:\/\/doi.org\/10.48550\/arXiv.2310.08118<\/a>.<\/li>\n<\/ul>\n
Ranking Foundation Models:<\/strong><\/p>\n\n- Boubdir, Meriem, Edward Kim, Beyza Ermis, Sara Hooker, and Marzieh Fadaee. \u201cElo Uncovered: Robustness and Best Practices in Language Model Evaluation.\u201d arXiv:2311.17295. Preprint, November 29, 2023. https:\/\/doi.org\/10.48550\/arXiv.2311.17295<\/a>.<\/li>\n
- Munos, R\u00e9mi, Michal Valko, Daniele Calandriello, et al. \u201cNash Learning from Human Feedback.\u201d arXiv:2312.00886. Preprint, June 11, 2024. https:\/\/doi.org\/10.48550\/arXiv.2312.00886<\/a>.<\/li>\n<\/ul>\n
Evaluation of AI systems:<\/strong><\/p>\n\n- Zhong, Wanjun, Ruixiang Cui, Yiduo Guo, et al. \u201cAGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.\u201d arXiv:2304.06364. Preprint, September 18, 2023. https:\/\/doi.org\/10.48550\/arXiv.2304.06364<\/a>.<\/li>\n
- Luo, Zheheng, Qianqian Xie, and Sophia Ananiadou. \u201cChatGPT as a Factual Inconsistency Evaluator for Text Summarization.\u201d arXiv:2303.15621. Preprint, April 13, 2023. https:\/\/doi.org\/10.48550\/arXiv.2303.15621<\/a>.<\/li>\n
- Sprague, Zayne, Xi Ye, Kaj Bostrom, Swarat Chaudhuri, and Greg Durrett. \u201cMuSR: Testing the Limits of Chain-of-Thought with Multistep Soft Reasoning.\u201d arXiv:2310.16049. Preprint, March 23, 2024. https:\/\/doi.org\/10.48550\/arXiv.2310.16049<\/a>.<\/li>\n<\/ul>\n
Prompt engineering, attacks, and defenses:<\/strong><\/p>\n\n- Huang, Jie, Hanyin Shao, and Kevin Chen-Chuan Chang. \u201cAre Large Pre-Trained Language Models Leaking Your Personal Information?\u201d arXiv:2205.12628. Preprint, October 20, 2022. https:\/\/doi.org\/10.48550\/arXiv.2205.12628<\/a>.<\/li>\n
- Carlini, Nicholas, Florian Tramer, Eric Wallace, et al. \u201cExtracting Training Data from Large Language Models.\u201d arXiv:2012.07805. Preprint, June 15, 2021. https:\/\/doi.org\/10.48550\/arXiv.2012.07805<\/a>.<\/li>\n
- Chao, Patrick, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong. \u201cJailbreaking Black Box Large Language Models in Twenty Queries.\u201d arXiv:2310.08419. Preprint, July 18, 2024. https:\/\/doi.org\/10.48550\/arXiv.2310.08419<\/a>.<\/li>\n
- Wallace, Eric, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. \u201cThe Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions.\u201d arXiv:2404.13208. Preprint, April 19, 2024. https:\/\/doi.org\/10.48550\/arXiv.2404.13208<\/a>.<\/li>\n
- Zou, Andy, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. \u201cUniversal and Transferable Adversarial Attacks on Aligned Language Models.\u201d arXiv:2307.15043. Preprint, December 20, 2023. https:\/\/doi.org\/10.48550\/arXiv.2307.15043<\/a>.<\/li>\n
- Zhu, Kaijie, Jindong Wang, Jiaheng Zhou, et al. \u201cPromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.\u201d arXiv:2306.04528. Preprint, July 16, 2024. https:\/\/doi.org\/10.48550\/arXiv.2306.04528<\/a>.<\/li>\n<\/ul>\n
Agents, patterns, RAG, and memory management:<\/strong><\/p>\n\n- Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. \u201cRetrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.\u201d arXiv:2005.11401. Preprint, April 12, 2021. https:\/\/doi.org\/10.48550\/arXiv.2005.11401<\/a>.<\/li>\n
- Shinn, Noah, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. \u201cReflexion: Language Agents with Verbal Reinforcement Learning.\u201d arXiv:2303.11366. Preprint, arXiv, October 10, 2023. https:\/\/doi.org\/10.48550\/arXiv.2303.11366<\/a>.<\/li>\n
- Yang, John, Carlos E. Jimenez, Alexander Wettig, et al. \u201cSWE-Agent: Agent-Computer Interfaces Enable Automated Software Engineering.\u201d arXiv:2405.15793. Preprint, arXiv, November 11, 2024. https:\/\/doi.org\/10.48550\/arXiv.2405.15793<\/a>.<\/li>\n
- Liu, Lei, Xiaoyan Yang, Yue Shen, et al. \u201cThink-in-Memory: Recalling and Post-Thinking Enable LLMs with Long-Term Memory.\u201d arXiv:2311.08719. Preprint, arXiv, November 15, 2023. https:\/\/doi.org\/10.48550\/arXiv.2311.08719<\/a>.<\/li>\n
- Bae, Sanghwan, Donghyun Kwak, Soyoung Kang, et al. \u201cKeep Me Updated! Memory Management in Long-Term Conversations.\u201d arXiv:2210.08750. Preprint, arXiv, October 17,2022. https:\/\/doi.org\/10.48550\/arXiv.2210.08750<\/a>.<\/li>\n<\/ul>\n
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Timeline<\/p>\n
2 hours<\/p>\n","protected":false},"excerpt":{"rendered":"
Speakers Jos\u00e9 Enrique Pons (NTTData) Abstract Having autonomous agents that solve complex problems and can make decisions in unexpected situations is promising. That\u2019s the reason why most businesses across various industries are currently exploring Agentic AI. This tutorial presents a research-based, practical guide for designing enterprise-ready multi-agent systems based on our experience in building…<\/p>\n","protected":false},"author":2627,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-292","page","type-page","status-publish","hentry"],"yoast_head":"\n
Engineering Trustworthy Multi-Agent Systems: A Deep Dive<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ieeesmc.org\/cai-2026\/tutorial-3-engineering-trustworthy-multi-agent-systems\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Engineering Trustworthy Multi-Agent Systems: A Deep Dive\" \/>\n<meta property=\"og:description\" content=\"Speakers Jos\u00e9 Enrique Pons (NTTData) Abstract Having autonomous agents that solve complex problems and can make decisions in unexpected situations is promising. That\u2019s the reason why most businesses across various industries are currently exploring Agentic AI. 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