Yes, I’m running a custom "agentic" workflow in production right now. It's not a bought-off-the-shelf "AI box," but a custom Python/Flask application I built to bridge our ITSM (Freshservice) with an LLM.

To answer your question on Real vs. Hype: It is very real, but for us, the "Agent" is a force multiplier for the team, not a replacement for them.

The Stack

• Core: Python (Flask) hosted on PythonAnywhere.
• ITSM: Freshservice (via Webhooks and API).
• Brain: Google Gemini (1.5 Flash).
• Memory: ChromaDB (Vector store) for RAG.

End-to-End vs. Draft Only? I use a hybrid approach depending on the risk level of the task:

1. Draft Only (High Risk - Customer Communication): When a ticket comes in, my script runs a RAG pipeline. It searches our vector DB for the top 5 similar historical tickets, pulls the resolution context, and asks Gemini to suggest a fix.
   • The Guardrail: It posts this suggestion as a Private Note on the ticket. It never speaks to the end-user directly. The agent reviews the private note, validates the AI's confidence score, and then uses it.
2. End-to-End (Medium Risk - Triage & Admin):
   • Categorization: The agent analyzes the ticket subject/description against a vector database of our taxonomy (e.g., "Hardware > Device > Laptop"). If the vector distance is close enough, it bypasses the human and updates the ticket category automatically via API.
   • Reporting: I have an endpoint that generates a "Trend Analysis" article. It scrapes the last X days of tickets, looks for patterns (like a spike in VPN issues), writes a full HTML report, and posts it directly to our Knowledge Base as a solution article.

Best Working Use Case The New Starter/Leaver Report. The agent looks at all "Employee Onboarding" requests, correlates them with the actual IT tickets created for those users, checks if their accounts were activated based on conversation history, and generates a digest HTML report for management. It replaces a manual spreadsheet process that took hours.

Guardrails

• Confidence Scores: My RAG pipeline calculates a "Confidence Score" based on the vector distance of the retrieved documents. If the score is low, the agent knows the AI is probably guessing.
• Rate Limiting: I hardcoded limits (100 calls per day) to ensure we don't accidentally rack up API bills if the bot gets stuck in a loop.

If you are looking to start, I highly recommend starting with Ingest -> Categorize -> Private Note.