Introduction: Why an AI agent is essential for customer support today
Overview: What does an AI agent for customer support offer
- Recognizes natural language and responds contextually
- Manages multiple conversations simultaneously thanks to Bot Mode
- Integrates with CRM systems, knowledge databases, and analytics tools
- Learns from past interactions to improve over time
Why choose Hermes Agent and Bot Mode in 2026
- MIT License
- Bot Mode
- Native integrationwith new language models like GLM-5.3 for deeper understanding
- Multimodal analysis
Creating your agent: A step-by-step guide
1. Install Hermes Agent and enable Bot Mode
# Clone the repository
$ git clone https://github.com/nousresearch/hermes-agent.git
$ cd hermes-agent
# Install dependencies
$ pip install -r requirements.txt
# Enable Bot Mode (edit config.yaml)
# bot_mode: true
# Start the agent
$ python -m hermes_agent.run --config config.yaml2. Design the main support prompt
You are a friendly customer-support agent for Acme Corp.
- Greet the user warmly and ask how you can help.
- Use clear, concise language and stay within the knowledge base.
- If you cannot answer, apologize and offer to transfer to a human agent.
- End every reply with a follow-up question to confirm satisfaction.
Knowledge base excerpt: {knowledge}
Current conversation:
{history}
User: {user_input}
Agent:3. Connect an advanced language model (GLM-5.3)
import requests
import json
def query_glm(prompt):
url = "https://api.zhipu.ai/v1/chat/completions"
headers = {
"Authorization": "Bearer YOUR_GLM_KEY",
"Content-Type": "application/json"
}
payload = {
"model": "GLM-5.3",
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.2
}
response = requests.post(url, headers=headers, data=json.dumps(payload))
return response.json()["choices"][0]["message"]["content"]4. Integration with wearable device data (Samsung Health)
import requests
def get_wearable_data(user_id):
url = f"https://api.samsunghealth.com/v1/users/{user_id}/biosignals"
headers = {"Authorization": "Bearer WEARABLE_KEY"}
resp = requests.get(url, headers=headers)
return resp.json()Complete workflow example
- Input:A customer asks about the measurement accuracy of a bracelet.
- Pre-processing:The message is passed through Hermes Agent's tokenizer.
- Research:The system queries the knowledge base and retrieves biosignal data via the Samsung Health API.
- Generation:The prompt is sent to GLM-5.3, which combines facts with data analysis.
- Output:The agent responds with a personalized explanation and an invitation to contact a human expert if necessary.
Best practices and tips for success
- Write domain-specific prompts.Include examples of phrases, jargon, and limitations.
- Test Bot Mode with a small group of users before launch.Verify response quality and conversation fairness.
- Monitor metrics.Track AHT, CSAT, escalation rate, and agent uptime.
- Update the knowledge base regularly.Add new products, FAQs, and release notes.
- Use multimodal analysis judiciously.Integrate wearable data only when relevant to the customer's request.
Conclusions
Concrete actions to take today
- Clone the Hermes Agent repository and enable Bot Mode.
- Adapt the example prompt to your brand and knowledge base.
- Integrate your GLM-5.3 API token and test a simple Q&A flow.
- Configure a connector for Samsung Health data (if applicable) and test data retrieval.
- Launch a pilot with 10 real users and measure AHT and CSAT before full rollout.