AI for Engineers Agentic AI Engineering Training

Agentic AI describes AI systems that plan, decide, and act on their own. Unlike traditional chatbots or RAG applications, agentic systems carry out multiple steps, call tools, work with memory and state, and adapt their approach based on intermediate results. This shifts how organisations use AI: instead of generating text, agents take on tasks with concrete business value.

Advances in large language models, tool calling, and orchestration frameworks have brought Agentic AI into production scenarios in 2026. Companies want to know when agents pay off, how to operate them safely, and which architectures hold up under scale, latency, and cost constraints. This training gives you the skills to answer those questions and to build agents from idea to operation.

The course targets software engineers, data scientists, ML and AI engineers, IT architects, and project or product managers with programming experience. You need a working knowledge of software development and Python. Experience with AI technologies or cloud platforms helps, but is not required.

Across two hands-on days, theory and practice alternate. You analyse when Agentic AI delivers real value, build agents using ReAct, tool calling, and multi-step reasoning, orchestrate workflows with cloud and open-source tools, and harden your solutions with guardrails, logging, and red teaming. Advanced topics include multi-agent systems, agentic RAG, and human-in-the-loop patterns.

For more AI training options, visit our tecnovy AI training overview.

• You understand how Agentic AI differs from chatbots and RAG applications.
• You recognise which tasks suit agentic solutions and which do not.
• You apply agent patterns like ReAct, tool calling, and multi-step reasoning.
• You design architectures for single-agent and multi-agent systems.
• You build working AI agents with Python and cloud tools.
• You connect external tools, APIs, and data sources to your agents.
• You evaluate and optimise agentic systems systematically.
• You implement security and governance concepts for Agentic AI.
• You work with advanced concepts like agentic RAG, human-in-the-loop, and multimodal agents.

On completion of the training, you receive a tecnovy course certificate. It documents your attendance, the duration, and the topics covered, and suits your CV, your LinkedIn profile, and internal skill records.

There is no external exam at the end of the course. The focus is on practical capability: you leave the training with code, architectures, and decision patterns you can apply directly in your own projects. Agentic AI does not yet have an established international certification body, so we deliberately focus on demonstrable practice rather than multiple-choice exams.