Description
“Loop Engineering for Agentic AI” is a course published by Udemy focused on building more reliable, flexible, and effective agentic AI systems. If you feel that relying solely on prompt engineering is insufficient for creating intelligent agents, this course offers a structured, hands-on approach. Moving beyond mere prompt engineering, you will learn to design iterative feedback loops that enable AI agents to execute tasks, evaluate results, identify issues, and refine their behavior through structured workflows. The course covers key concepts such as agent loops, planning, execution, evaluation, feedback, tool usage, context management, error handling, and iterative optimization.
You will also learn the distinction between agentic loops and single Large Language Model (LLM) calls, and why this distinction is crucial for building real-world systems. Through practical examples, you will build a tool-enabled agent loop using Python and learn how to manage agent state, memory, retrieval points, and context. These skills will help you create systems that maintain their course without becoming chaotic during execution. Furthermore, you will learn to design safe, reliable termination conditions and necessary safeguards to ensure agents do not spiral out of control or get trapped in infinite loops, thereby maintaining predictable behavior. With this approach, your agentic systems can continuously evaluate their work and respond to changing conditions rather than simply generating a static output. This course is suitable for beginners. By the end of the course, you will have a clear understanding of designing agent-based loops and will be able to build smarter, more robust, and more reliable systems.
What you will learn in Loop Engineering for Agentic AI:
- Explain how agentic loops differ from single LLM calls
- Build a tool-calling agent loop using Python
- Manage agent state, memory, checkpoints, and context
- Design reliable termination conditions and guardrails
- and …
Course specifications
Publisher: Udemy
Instructors: Arjun Vaid and School of AI
Language: English
Level: Introductory to Advanced
Number of Lessons: 75
Duration: 5 hours and 49 minutes
Course topics
Loop Engineering for Agentic AI Prerequisites
Basic Python programming knowledge is helpful
A computer capable of running Python and a code editor
Basic familiarity with LLMs and prompting
No previous experience building AI agents is required
No paid AI subscription is required for the core exercises
Pictures
Loop Engineering for Agentic AI introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 2160p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
9.1 GB