Скачать или смотреть Agentic AI: Deep Dive Advanced Reasoning Techniques with LLMs. CoT and React

In this video, I'll show you an artificial intelligence application that demonstrates advanced reasoning techniques with LLMs.

What's this project about?

Planning an entire vacation is complex: choosing activities, checking the weather, controlling the budget, and making sure it all makes sense. *AgentsVille Trip Planner* does this automatically, using specialized AI agents working together.

What makes this application special?

This project isn't just another chatbot. It's a sophisticated demonstration of how different AI reasoning techniques combine to create robust and reliable systems:

**1. Chain-of-Thought (CoT) Reasoning**: It breaks down the problem step by step, analyzing requirements, available activities, weather, and budget before generating an itinerary, just like a travel expert would.

**2. ReAct Pattern (Reason + Act)**: An agent that thinks and acts iteratively. Evaluate the plan, identify problems, use specific tools to correct them, and repeat the cycle until you achieve a perfect itinerary.

**3. Multi-Layer Evaluation System**: Each itinerary is validated against six criteria: correct dates, budget respect, real-world activities without hallucinations, weather compatibility, interest coverage, and the appropriate number of activities per day.

Why is this approach important?

In the real world of enterprise AI, it's not enough to simply generate answers. We need reliable, validable, and self-correcting systems.

This project demonstrates how to:
Design specialized agents for specific tasks
Implement feedback loops that iteratively improve
Establish guardrails through schema validation
Orchestrate multiple reasoning techniques into a coherent flow

What will we see?

We will explore the system architecture with two specialized agents, advanced prompting techniques in action, the evaluation framework that ensures quality, and the complete flow from user input to a validated itinerary.

This project serves as a *blueprint* for building production AI Agent applications, where reliability and transparency are key.

*Join me on this journey where we'll see how artificial intelligence can go beyond simple answers to become a complex, adaptive, and robust reasoning system.*

---

Production Notes

*Visual Elements:*
Architecture diagrams and agent spectrum
Live CLI demo
Input/output JSON examples

*Key Points:*
✅ Two phases: Generation (CoT) + Refinement (ReAct)
✅ Robust multi-layer validation
✅ Iterative self-correction
✅ Reusable patterns

Notebook and code for paid subscribers only. Contact the administrator for access. [email protected]
https://github.com/olonok69/Agentic_A...