ServiceNow Incident Copilot

Zero-touch L1 incident triage: new incidents are automatically classified by Azure OpenAI, grounded in company knowledge embeddings, and routed to the right enterprise team in ServiceNow — no manual effort. Exposed as both a FastAPI service and an MCP server (connect it to Claude Desktop and triage incidents conversationally).

Live demo

▶ Live app: https://servicenow-incident-copilot.onrender.com/docs — interactive API docs (free tier; first load may take ~50s if asleep, then it's instant)

The recording above is the real APP_MODE=mock pipeline — deterministic, credential-free, and exactly what CI runs.

Deployed in mock mode — the entire pipeline runs offline with a deterministic LLM and an in-memory ServiceNow, so no credentials are needed and nothing sensitive is exposed. Try it:

• /docs — interactive Swagger UI; click "Try it out" on POST /demo/run-all to auto-triage six incidents

• /stats — live routing-accuracy dashboard (JSON)

Hosted on Render's free tier, which sleeps after inactivity — the first request may take ~50s to wake, then it's instant. The GIF above is the same output with no wait.

Related MCP server: ServiceNow MCP Server

Why I built this

I'm drawn to the intersection of ServiceNow and applied AI, and I wanted to see what "AI integration" should really look like inside an ITSM platform — not a chatbot bolted onto the side, but AI wired into the operational flow where it removes genuine toil. L1 triage is the ideal target: high-volume, repetitive, and every misroute burns an SLA. So instead of a demo snippet, I built the entire zero-touch loop the way I'd want to run it in production — webhook in, RAG-grounded decision, write-back with an audit note — and pushed on the parts that are easy to hand-wave and hard to get right:

• It runs with zero credentials. The offline APP_MODE=mock stack implements the exact same interfaces as the live one (in-memory ServiceNow + a rule-based LLM double), so the pipeline can't tell the difference — and neither can pytest. I wanted anyone to be able to clone it and see it work in 60 seconds, and I wanted CI to exercise the real orchestration, not a stub.

• The AI is accountable, not magic. The model can only route to teams in the catalog; a hallucinated team name zeros the confidence; anything under the threshold parks in a human queue with the full reasoning written to the incident's work note. I care more about trustworthy automation than a flashy accuracy number.

• It's provider- and infra-agnostic on purpose. Swapping the LLM (Azure OpenAI ↔ OpenAI) or the vector store (in-memory ↔ ChromaDB ↔ Azure AI Search) is one env var, because everything depends on a protocol, not a concrete class. That's the difference between a proof-of-concept and something a team could actually adopt.

I built it in visible phases (see the commit history) — mock-first, then observability, then a second vector backend, then the MCP surface — because that's how I like to work: get a thin end-to-end slice running, then deepen it one honest layer at a time.

How the zero-touch pipeline works

 ServiceNow                    Incident Copilot (FastAPI)                ServiceNow
┌───────────┐  Business Rule  ┌─────────────────────────────────┐  PATCH  ┌───────────┐
│ Incident   │───(webhook)───▶│ 1. Fetch incident (Table API)     │───────▶│ assignment │
│ created    │                │ 2. Embed & retrieve:              │        │ _group set │
└───────────┘                │    • company KB articles           │        │ + priority │
                              │    • similar past incidents        │        │ + category │
                              │      (with historical routing)     │        │ + AI work  │
                              │ 3. Azure OpenAI triage             │        │   note     │
                              │    (forced function calling →      │        └───────────┘
                              │     Pydantic-validated output)     │
                              │ 4. Confidence gate:                │
                              │    ≥ 0.7 → auto-route              │
                              │    < 0.7 → park in L1 queue        │
                              └─────────────────────────────────┘

Design decisions worth noting:

• One IncidentService layer serves both the REST API and the MCP tools — no duplicated logic.

• The LLM tool schema is generated from the TriageResult Pydantic model, so the AI contract and app contract can't drift.

• Guardrails, not vibes: the LLM can only route to teams in the catalog; hallucinated team names zero the confidence; low confidence falls back to a human queue with full reasoning in the work note.

• Feedback loop: POST /learn/{sys_id} indexes resolved incidents back into the vector store, so routing accuracy improves with history.

Stack

FastAPI · Pydantic v2 · httpx (async) · OAuth 2.0 · ServiceNow Table API · Azure OpenAI (chat + embeddings, standard OpenAI fallback) · ChromaDB (swappable for Azure AI Search) · MCP (FastMCP) · Docker Compose · pytest + respx · GitHub Actions · structured JSON logging

Quick start

60-second demo, zero credentials (mock mode)

pip install -r requirements.txt
APP_MODE=mock uvicorn app.main:app --port 8000
curl -X POST localhost:8000/demo/run-all | jq

Six realistic incidents get triaged and routed instantly by a deterministic offline stack (in-memory ServiceNow + rule-based LLM double) that implements the exact same interfaces as the live one — the pipeline can't tell the difference.

Live mode (real PDI + Azure OpenAI)

cp .env.example .env          # fill in PDI + OpenAI credentials
pip install -r requirements.txt -r requirements-dev.txt
python -m scripts.seed_data --snow   # index KB + history, create demo incidents
uvicorn app.main:app --reload

Zero-touch setup: create the Business Rule + Outbound REST Message from integration/servicenow_business_rule.js (use ngrok http 8000 locally). Now every new incident routes itself.

Manual demo without the webhook:

curl -X POST localhost:8000/triage/<sys_id> | jq

Observability

Every request gets a correlation ID (honored from an inbound X-Correlation-ID header — e.g. propagated from MuleSoft — or minted fresh) that tags every log line in that incident's journey and is echoed back in the response header. Grep one cid to trace a single incident end to end.

GET /stats returns a live routing dashboard:

{
  "processed": 6, "auto_routed": 6, "auto_route_rate": 1.0,
  "low_confidence_fallbacks": 0, "avg_confidence": 0.84,
  "by_group": { "Network Operations": 1, "Security Operations": 1, ... },
  "by_priority": { "P1": 1, "P2": 3, "P3": 2 },
  "avg_stage_ms": { "retrieval": 0.6, "triage": 1.0, "writeback": 0.1 }
}

The auto_route_rate and per-group distribution are the metrics you'd watch to tune the confidence threshold. Counters map 1:1 onto Prometheus if real scraping is needed.

Swappable vector backend

Three implementations satisfy one VectorStore protocol (app/services/vector_store.py):

Switching is one env var — IncidentService never changes, because it depends on the protocol, not a concrete store. The Azure backend creates its indexes idempotently on startup and pushes our own text-embedding-3-small vectors, so retrieval quality is identical across backends. A test asserts all three expose the same method signatures.

MCP server (Claude Desktop)

Copy integration/claude_desktop_config.example.json into your Claude Desktop config (set the absolute cwd). With "APP_MODE": "mock" it works with zero credentials — flip to live once the PDI is wired.

Then ask Claude: "Triage the incident about the CEO gift card email and explain your routing" or "What are the routing stats for this session?"

A full 3-minute recording script lives in integration/DEMO_SCRIPT.md.

Docker

docker compose up --build

Tests

pytest -v    # ServiceNow mocked with respx, LLM mocked — no credentials needed

Enterprise topology (MuleSoft)

See integration/MULESOFT_DESIGN.md — API-led connectivity design with a servicenow-sapi System API owning credentials and policies. The client here is a one-file swap away from pointing at CloudHub.

Roadmap

• Azure AI Search backend ✅ done — see VECTOR_BACKEND=azure_ai_search

• Multi-turn clarification: agent asks the caller for missing details via chat/email

• Routing accuracy dashboard (auto-routed vs. reassigned rate)