Uploaded August 2026 | Updated September 2026, 2 weeks ago
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
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What is loop engineering? If you have ever built a ReAct agent, you have already used it without knowing the name. In this video I break loop engineering down step by step and show where it fits next to prompt, context, and harness engineering.
Blog Link: kgptalkie.com/tutorials/generative-ai/loop-engineering
You will learn how a ReAct agent works, why the first three types of engineering sit on the input side of your model while loop engineering sits on the output side, why the evaluator has to be a separate model (a model cannot judge its own output without bias), what self-prompting really means, and why memory should live outside the agent state so multiple agents can share it. This is a complete concept video for developers working with AI agents. You do not need any prior knowledge of loop engineering.
⏱ Chapters:
0:00 Intro
0:19 ReAct Agent Recap
1:03 Prompt vs Context vs Harness vs Loop Engineering
2:01 From ReAct Agent to Loop Engineering
3:07 Why You Need a Second Model
4:36 What Self-Prompting Really Means
5:26 Shared Memory Outside the Agent State
6:08 Coming Next: Graph Engineering
🔗 Resources:
Presentation slides: slideshare.net/slideshow/mastering-loop-engineering-in-ai-agents-for-banking-finance/289019563#1
🎓 Go deeper with my Udemy course:
Master LangGraph v1 and Ollama - Build Gen AI Agents
kgptalkie.com/langgraph
If this video helped, give it a like. If you got stuck anywhere, tell me in the comments and I will help. Subscribe and hit the bell so you do not miss the graph engineering video coming next.
#LoopEngineering #AIAgents #AgenticAI
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
----------
What is loop engineering? If you have ever built a ReAct agent, you have already used it without knowing the name. In this video I break loop engineering down step by step and show where it fits next to prompt, context, and harness engineering.
Blog Link: kgptalkie.com/tutorials/generative-ai/loop-engineering
You will learn how a ReAct agent works, why the first three types of engineering sit on the input side of your model while loop engineering sits on the output side, why the evaluator has to be a separate model (a model cannot judge its own output without bias), what self-prompting really means, and why memory should live outside the agent state so multiple agents can share it. This is a complete concept video for developers working with AI agents. You do not need any prior knowledge of loop engineering.
⏱ Chapters:
0:00 Intro
0:19 ReAct Agent Recap
1:03 Prompt vs Context vs Harness vs Loop Engineering
2:01 From ReAct Agent to Loop Engineering
3:07 Why You Need a Second Model
4:36 What Self-Prompting Really Means
5:26 Shared Memory Outside the Agent State
6:08 Coming Next: Graph Engineering
🔗 Resources:
Presentation slides: slideshare.net/slideshow/mastering-loop-engineering-in-ai-agents-for-banking-finance/289019563#1
🎓 Go deeper with my Udemy course:
Master LangGraph v1 and Ollama - Build Gen AI Agents
kgptalkie.com/langgraph
If this video helped, give it a like. If you got stuck anywhere, tell me in the comments and I will help. Subscribe and hit the bell so you do not miss the graph engineering video coming next.
#LoopEngineering #AIAgents #AgenticAI
