awesome-chatgpt

### AI Agent Design for Customer Support Automation

**Agent Architecture:**
The agent is built as a Langflow-compatible workflow with three core components:
1. **Intent Recognition**: Uses a fine-tuned DistilBERT model to classify incoming queries into 5 categories (billing, technical support, account management, product info, and general inquiries). The model achieves 92% accuracy on our internal test set.
2. **Response Generation**: A retrieval-augmented generation (RAG) pipeline pulls from a vector database of 12,000 FAQ articles and 400 product documentation pages. For billing queries, it cross-references with the CRM to fetch real-time account data.
3. **Escalation Logic**: Queries classified as 'technical support' or requiring account-specific data are automatically routed to a human agent via Slack API integration. The agent tracks escalation rates and response times in real-time.

**Key Components:**
- **Langflow Integration**: The workflow is deployed as a Langflow agent with a REST API endpoint (`/api/agent/v1/support`). The API accepts JSON payloads with fields: `query` (string), `user_id` (string), and `session_id` (string).
- **Error Handling**: Implements three layers of error recovery:
  - **Fallback Responses**: If intent classification confidence < 0.7, the agent responds with a generic "I’m not sure I understand. Let me connect you to an expert" message.
  - **Retry Mechanism**: For RAG retrieval failures, the agent retries up to 3 times with exponential backoff before escalating.
  - **Graceful Degradation**: If the CRM API is down, the agent falls back to cached account data (updated hourly) and flags the issue for the operations team.
- **Logging & Metrics**: Tracks 8 key metrics in Prometheus:
  - Average response time (goal: < 2 seconds)
  - Intent classification confidence scores
  - Escalation rate (target: < 15%)
  - RAG retrieval success rate
  - API error rates (target: < 1%)
  - User satisfaction scores (collected via post-chat surveys)
  - Session duration
  - Agent uptime (target: 99.9%)

**Deployment Steps:**
1. **Model Training**: Fine-tune the DistilBERT model on your historical support tickets using the provided dataset in `./data/training/tickets.json`. Use the script `train_intent_model.py` with parameters `--epochs 10 --batch_size 32`.
2. **Vector Database Setup**: Populate the vector database using `build_faq_embeddings.py`, which processes FAQ articles from `./data/faqs/` and product docs from `./data/docs/`. Ensure the database is hosted on a server with 16GB RAM for optimal performance.
3. **Langflow Configuration**: Import the workflow template from `./langflow/support_agent.json` into Langflow. Configure the API endpoint in `config/api_settings.yaml` with your Slack bot token and CRM API credentials.
4. **Testing**: Run the agent in test mode using `test_agent.py --mode sandbox` to simulate 100 support queries. Validate that escalation logic works correctly by testing with queries like "Why is my invoice $500 higher than last month?"
5. **Production Deployment**: Deploy the Langflow agent to a Kubernetes cluster using the provided Helm chart in `./k8s/`. Monitor the Prometheus metrics for the first 48 hours to ensure performance targets are met before full rollout.

**Next Steps:**
- Schedule a review with the support team to gather feedback on the agent’s responses.
- Adjust the intent classification model based on real-world performance data after 2 weeks.
- Explore adding a sentiment analysis layer to detect frustrated users and prioritize their queries.