96488 - Agentic AI Bootcamp
Course Overview
This program is offered through Data Science Dojo.
Learn to build systems that reason
Learn to build agents, not just apps. Automate reasoning, planning, context retrieval and execution.
The Agentic AI Bootcamp is an intensive, hands-on program focused on building intelligent, context-aware applications using large language models and multi-agent systems. Participants will explore key tools and techniques including LangGraph, vector databases, agent interoperability, and advanced design patterns like ReAct, Reflection, and CodeAct.
What You'll Learn
- Understand and apply context-aware design using Model I/O, retrieval chains, and memory components to build intelligent LLM applications.
- Develop skills in implementing various memory types (buffer, summarization, vector-backed) to create applications that retain and use past context effectively.
- Gain hands-on experience with vector databases, including search strategies, indexing methods, and hybrid retrieval techniques for optimized performance.
- Build and orchestrate multi-agent systems that collaborate on complex tasks using tools like LangChain agents and LangGraph.
- Design and implement graph-based workflows with LangGraph, incorporating async execution, stateful transitions, and memory-aware routing.
- Apply reusable agentic design patterns such as ReAct, Reflection, and CodeAct to enable dynamic reasoning and autonomous LLM behavior.
- Enable agent interoperability across platforms and tools, including Google A2A, by designing API-ready, language-agnostic agent systems.
- Integrate conditional logic, looping, and modular task design to create flexible and adaptive agent workflows.
- Monitor and evaluate agent performance using observability tools such as logging, tracing, token tracking, and visual debugging interfaces.
- Deploy real-world agentic solutions with best practices in reliability, cost-efficiency, and scalability for production environments.
Who Should Attend
This bootcamp is ideal for professionals and advanced learners who want to build cutting-edge AI applications using large language models (LLMs) and agentic frameworks. It’s especially well-suited for:
- AI/ML Engineers looking to implement multi-agent systems and context-aware applications.
- Data Scientists interested in moving from analysis to intelligent application design.
- Software Developers building with LLM APIs and integrating AI workflows into products.
- Technical Product Managers overseeing AI initiatives and wanting a deeper understanding of agentic architecture.
- Startup Founders and innovators creating AI-driven tools, platforms, or services.
- R&D Teams exploring intelligent automation, orchestration, and LLM capabilities.
- Experienced Prompt Engineers ready to take the next step into tool-based agent design.
- DevOps and MLOps Engineers managing AI systems in production and needing observability skills.
- Academic Researchers focused on human-AI collaboration, autonomous systems, or cognitive architectures.
- Prior experience with Python and a basic understanding of LLMs is recommended to get the most out of the program.
Prerequisites
Very basic LLM fundamentals and python programming language knowledge required.UNM Tuition Remission
UNM Staff, Faculty, and Retirees: This course is Tuition Remission eligible under Professional Development. For more information, visit the UNM Tuition Remission information page. To see UNM HR's Tuition Remission for eligibility and tax liabilities, click here
This program is offered through Data Science Dojo.
Over the past few years, Data Science Dojo has earned global recognition for its high-quality instruction and distinctive approach to teaching data science. The organization continually updates its curriculum to reflect industry trends and ensure learners develop advanced, in-demand skills.
With the support of experienced instructors and mentors, Data Science Dojo fosters a culture where data science is used to benefit the broader community.
Data Science Dojo’s mission is to make data science more accessible, practical, and easier to learn for everyone.