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The #1 agent architecture of 2025 — explained simply and built cleanly.
AI agents have advanced dramatically in the past two years. In 2025, the most reliable and widely adopted agent design isn’t a “chatbot” at all — it’s the ReAct pattern, short for Reasoning + Acting. This architecture powers nearly 46% of all deployed production agents, from customer support systems to internal automation tools.
In this tutorial, you'll learn how to build a customer support agent using the ReAct pattern with the YG3 API, an OpenAI-compatible API designed specifically for production-grade applications. The tutorial focuses on concepts and architecture, and you can access the full code implementation directly on GitHub.
Most chatbots try to answer questions by predicting text — which works until they’re asked about something factual, specific, or operational. They hallucinate. They guess. They fail.
ReAct avoids all of this by splitting intelligence into two layers:
This allows the model to:
Instead of guessing, the agent uses tools to retrieve facts — making it dramatically more accurate than traditional chatbots.
In this tutorial, the customer support agent will be able to:
This is the foundation for any production-ready support automation system.
A ReAct agent follows a four-step loop:
The agent reads the question and determines whether it needs more information.
The model thinks:
“Do I know the answer? Or should I call a tool?”
If needed, it triggers a tool such as:
search_knowledge_base()check_order_status()get_user_account()lookup_subscription()These tools connect to your systems.
The agent synthesizes the tool results and produces a polished, accurate response.
This architecture is extremely flexible and can be applied to sales, analytics, reporting, CRM workflows, and more.
The YG3 API is fully OpenAI-compatible, meaning:
Integrating tools with YG3 is straightforward — making it perfect for production-grade ReAct agents.
Customer: “What’s your return policy?”
A ReAct agent:
search_knowledge_base("return_policy")Customer: “Where is my order ORD-12345?”
The agent:
check_order_status("ORD-12345")This is what modern support feels like: fast, accurate, and automated.
The accompanying project includes a Gradio interface featuring:
You can run the demo locally or in Colab.
The full runnable code is available on GitHub below.
Companies implementing ReAct agents typically see:
MetricImpactAutomation Rate60–80%Response Time70% fasterOperational Cost40% lowerCSAT Scores85–90%Availability24/7
A mid-sized company example:
Net Year 1 Savings: $80k
For real deployments, you may want to add:
All major orchestration frameworks — LangChain, LangGraph, CrewAI — work seamlessly with the YG3 API.
Below is the embedded GitHub project.
You can also click through to view the entire tutorial, runnable notebook, and full implementation.
You can now:
