A Practical Tutorial on Agentic AI
Year: 2026
Type: preprint
Abstract: Agentic AI is the most recent AI paradigm that extends the capabilities of large language models (LLMs) by providing access to external tools for performing specific tasks, external information resources for grounding responses rather than relying solely on model knowledge, and persistent task state and memory. These capabilities allow AI systems to plan, act, and adapt to intermediate results, whilst maintaining progress and coordinating specialised agents, allowing complex tasks to be completed with varying degrees of autonomy. For example, an agentic systematic-review assistant can search the web for relevant papers, screen and analyse them, and synthesise the evidence into a PRISMA-informed review. Research and development in agentic AI have expanded rapidly, accompanied by a growing range of general-purpose and specialised agent systems and development frameworks. Although several recent reviews have examined frameworks, evaluation, taxonomies and applications, practical hands-on guidance for researchers and practitioners seeking to understand and build agentic systems remains limited. This paper addresses this gap by providing a practical, implementation-oriented tutorial for building agentic AI systems using plain Python as well as three representative frameworks: LangGraph, CrewAI and the OpenAI Agents SDK. We discuss the progression from basic LLMs to agentic AI, explain the core components of agentic systems and examine agentic workflows and control patterns through which these components are combined to solve tasks; the tutorial then addresses evaluation at the component, trajectory and task levels, together with robustness, failure recovery and practical design guidance. Finally, we implement a research assistant as a case study to illustrate how agentic AI systems can be built, evaluated and safeguarded, supported by concise code skeletons in the paper and complete runnable implementations and additional end-to-end case studies in the accompanying repository at https://github.com/jmdvinodjmd/agentic-ai-tutorial.
Source: Preprints.org
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204167173