Value driven AI agents working together to deliver exceptional customer service

In today’s world, customer service demands more than just responses – it demands context, specialization, and coordination. For this reason we built the project customer-support-agentic-ai, a multi-agent system integrating specialized agents (Orders, Technical Support, Product, Solutions) under a central Orchestrator, aimed at delivering a seamless customer experience.
The system:
Typical customer-support workflows suffer from:
This project addresses all these by orchestrating specialist agents while preserving session memory and producing a unified customer-facing output.
At its core:
A central Orchestrator Agent receives the user message, analyzes intent & context, plans which specialist agents to invoke (and in what sequence or parallel).
Specialist agents handle domain tasks:
The Orchestrator then synthesizes all specialist outputs into a cohesive response for the user; simultaneously logs into a monitoring / logging system and updates session memory.
Here’s a visual of the flow:

(You can include the full diagram in your README or blog post.)
Clone the repository:
git clone https://github.com/Etheal9/customer-support-agentic-ai.git cd customer-support-agentic-ai
Set up and install dependencies (Python 3.10+):
python -m venv venv source venv/bin/activate # or appropriate for your OS pip install -r requirements.txt
(Optional) Configure API keys by copying .env.example → .env and filling in OPENAI_API_KEY and GEMINI_API_KEY.
If omitted, the system will run in mock mode (demo data only).
Choose your interface:
Streamlit UI (recommended):
streamlit run streamlit_app.py
FastAPI backend:
python main.py
Access docs at http://localhost:8000/docs.
Current limitations:
Planned improvements:
This project will be useful for:
Just plug it in, run the demo, inspect the code and logging, and you’re ready to adapt to your domain.
The customer-support-agentic-ai system is more than a demo: it’s a blueprint for next-generation, coordinated AI customer support. With clear architecture, ready to deploy demo, and an extensible design, you’re well positioned to move from proof-of-concept to production. Dive in, explore the agents, view the execution plans and chat interface—and let this system inspire your next customer-service innovation.
If you’d like, I can format this publication specifically for LinkedIn post length (shorter, punchy) or Medium blog (with visuals, headings, and callouts). Do you prefer one of those formats?