About agentcrafts
Our Mission
Empowering Intelligent Automation
agentcrafts is dedicated to equipping professionals and enthusiasts with the knowledge and skills required to develop autonomous AI agents. Based in Zurich, our curriculum combines research-driven content, practical projects, and expert mentorship to ensure participants can design, implement, and fine-tune intelligent systems tailored to a variety of real-world scenarios.
Hands-On Learning
Our courses offer immersive workshops, collaborative labs, and live coding sessions. Whether you choose in-person classes at our Bahnhofstrasse facility or remote participation, every module emphasizes active experimentation and peer learning to reinforce critical concepts and best practices.
Community and Support
Beyond instruction, agentcrafts fosters a vibrant community of AI practitioners. Participants gain access to discussion forums, code repositories, and periodic faculty office hours, ensuring ongoing collaboration and support as they apply new skills in professional or academic settings.
Course Highlights
At agentcrafts, we believe that the future of intelligent automation relies on specialized AI agents tailored to specific domains. Our curated course series dives deep into the art and science of designing, training, and deploying autonomous agents that excel in research, strategic planning, and real-world service tasks. Participants gain hands-on experience constructing neural models, integrating advanced language understanding pipelines, and orchestrating decision-making workflows. With guided workshops, interactive labs, and expert-led seminars, learners refine skills essential for developing robust agents capable of analyzing complex datasets, generating adaptive strategies, and engaging users with context-aware support. By the end of this module, each student will have crafted a functional prototype agent, mastered evaluation metrics, and acquired best practices for iterative improvement, setting a solid foundation for tackling diverse challenges in AI-driven environments.
Research Agent Foundations equips learners with the theoretical and practical frameworks needed to build AI agents that excel at information gathering, hypothesis generation, and data analysis. In this in-depth segment, participants explore probabilistic reasoning, knowledge representation techniques, and advanced retrieval methods. Hands-on labs guide you through constructing pipelines that ingest unstructured documents, extract key insights, and assemble coherent research briefs. By leveraging transformer-based models alongside custom evaluation protocols, learners gain the capacity to automate literature reviews, synthesize cross-domain findings, and prioritize research avenues based on dynamic criteria. This intensive module lays the groundwork for deploying intelligent assistants capable of accelerating discovery and supporting evidence-driven decision processes in academic and industrial settings.
Strategic Planning Agents delves into algorithmic design principles that enable AI systems to formulate and adapt multi-step plans in complex environments. Through interactive projects, students implement utility-driven architectures, apply search and optimization algorithms, and integrate real-time feedback loops. This curriculum covers hierarchical task decomposition, constraint management, and risk assessment, empowering agents to adjust strategies dynamically as conditions evolve. Learners experiment with simulation-based testing, performance tuning, and scenario modeling to ensure robust plan generation under uncertainty. By completing this course, participants will command a toolkit for creating agents that can automate scheduling, resource allocation, and decision support processes across diverse operational contexts.
Service and Support Bots focuses on designing AI agents that engage directly with end users to deliver efficient, personalized assistance. This module introduces conversation design, context tracking, and sentiment-aware response generation techniques. Participants build chat interfaces, integrate external APIs, and implement stateful dialogue frameworks to ensure smooth, coherent interactions. Coursework includes session management, fallback handling, and continuous learning through user feedback loops. By combining rule-based elements with adaptive machine learning models, learners create service agents capable of handling inquiries, executing tasks, and escalating complex issues seamlessly. The practical exercises ensure graduates can deploy user-centric bots for customer care, help desks, and operational support functions in real-world settings.