AI-powered Agent Assist system built with n8n and OpenAI to transform raw support tickets into structured, actionable guidance for agents in real time.
Support teams are under constant pressure to respond quickly, accurately, and consistently.
In most organizations:
- Agents manually interpret ticket context
- Response quality varies across team members
- Escalation risks are identified too late
- Knowledge is not consistently applied
This project introduces an AI-powered Agent Assist Engine that augments support agents with:
- Real-time ticket understanding
- Structured guidance and recommendations
- Response drafting assistance
- Escalation awareness
The result is a system that improves both speed and quality of support interactions.
This is Project 8 in a broader system:
- Project 1 → AI Ticket Triage
- Project 2 → Escalation Risk Detection
- Project 3 → SLA Breach Prediction
- Project 4 → Weekly Support Intelligence Report
- Project 5 → AI Response Suggestion
- Project 6 → AI Support RCA Engine
- Project 7 → AI RCA Operating Model
- Project 8 → AI Agent Assist Engine (this project)
This system follows a structured pipeline:
- Ticket ingestion
- AI enrichment
- Structured parsing
- Context merging
- Dual output delivery
This project builds on a multi-stage AI-powered support system designed to evolve teams from reactive support to intelligence-driven operations.
Classifies incoming support tickets and structures data for downstream workflows.
👉 https://github.com/jesseautomates/ai-support-ticket-triage-automation
Identifies tickets likely to escalate using AI signals.
👉 https://github.com/jesseautomates/ai-support-escalation-risk-detection
Predicts SLA risk before deadlines are missed.
👉 https://github.com/jesseautomates/ai-sla-breach-prediction
Generates structured leadership insights from support data.
👉 https://github.com/jesseautomates/ai-support-intelligence-report/
Generates context-aware responses to assist agents.
👉 https://github.com/jesseautomates/ai-support-response-suggestion
Identifies root causes and separates known vs unknown issues.
👉 https://github.com/jesseautomates/ai-support-rca-engine
Transforms RCA insights into operational actions and leadership intelligence.
👉 https://github.com/jesseautomates/ai-support-rca-operating-model
Provides real-time AI-powered guidance and response drafting for support agents.
Together, these projects form a layered system:
Triage → Risk Detection → SLA Prediction → Intelligence → Agent Assistance → RCA → Operational Execution
- Analyzes incoming support tickets using AI
- Generates structured summaries of customer issues
- Detects customer sentiment
- Identifies escalation risk
- Recommends next best actions for agents
- Drafts customer-ready responses
- Produces structured outputs for analytics and reporting
- Enables both real-time and batch processing
- Pulls ticket data from Google Sheets / CRM / Helpdesk
- Includes fields such as:
- ticket_id
- subject
- message
- customer details
- Normalizes and structures input data
- Prepares payload for AI processing
The core intelligence layer generates:
- Issue summarization
- Sentiment detection
- Escalation risk scoring
- Recommended action generation
- Response drafting
- Enforces consistent JSON schema
- Ensures clean, structured outputs
- Extracts JSON from model output
- Handles formatting inconsistencies
- Converts AI output into usable structured data
- Merges:
- Original ticket data
- AI-generated insights
- Produces a complete, enriched support record
- Appends results to Google Sheets
- Enables:
- Reporting
- Dashboards
- Trend analysis
- Human-readable format for agents
- Includes:
- Summary
- Sentiment + risk
- Recommended action
- Suggested response
Simulates how an agent would consume AI assistance in real time.
- n8n (workflow orchestration)
- OpenAI API (AI processing + response generation)
- Google Sheets (data source + output logging)
- Import workflow JSON into n8n
- Connect Google Sheets dataset
- Configure OpenAI credentials
- Define prompt structure and schema
- Map output fields to Google Sheets
- Test with sample dataset
- Scale to batch or webhook-based processing
- Demonstrates AI-powered agent assistance at scale
- Converts unstructured tickets into structured guidance
- Bridges the gap between AI output and operational usability
- Supports both real-time and batch workflows
- Improves agent efficiency and response consistency
Most AI implementations stop at generating responses.
This system shows how to:
Turn AI into a usable decision-support layer for frontline teams.