Agentic AI Helpdesk Platform

Autonomous Ticket Resolution with Multi-Agent Decision Making

Overview

This project is an agentic AI-powered helpdesk platform designed to autonomously resolve customer support tickets using a structured multi-agent architecture.

Unlike traditional rule-based systems or single-LLM chatbots, this platform decomposes the support workflow into specialized agents that classify, plan, resolve, verify, and escalate tickets. Each agent operates with a clearly defined role and decision boundary, enabling reliable automation, transparency, and measurable performance.

The system prioritizes correctness, cost efficiency, and controlled escalation over generic AI responses.

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Core Features

• Multi-agent orchestration with explicit roles
• Autonomous ticket classification and prioritization
• Decision-driven resolution or escalation
• Tool-augmented problem solving (KB, APIs, scripts)
• Solution verification before ticket closure
• Learning from past failures and escalations
• End-to-end observability with metrics and logs

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High-Level Architecture

User Ticket
↓
Classifier Agent
↓
Planner Agent
├── Automatic Resolution
├── Clarification Request
└── Human Escalation
↓
Verification Agent
↓
Ticket Closure or Escalation

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Agent Responsibilities

Classifier Agent

• Identifies issue domain and category
• Estimates urgency and complexity
• Provides structured input to the Planner Agent

Planner Agent

• Determines the next action for a ticket:
  • Resolve automatically
  • Request additional information
  • Escalate to a human agent
• Acts as the central decision-making component

Resolution Agent

• Executes the selected resolution strategy
• Uses external tools such as:
  • Knowledge base retrieval
  • API integrations
  • Automated scripts and workflows

Verification Agent

• Validates the proposed solution
• Checks correctness, completeness, and confidence
• Prevents premature or incorrect ticket closure

Escalation Agent

• Transfers tickets to human agents when:
  • Confidence is below threshold
  • Resolution attempts fail
  • Policy or safety constraints apply

Learning Agent

• Analyzes historical failures and escalations
• Improves future planning decisions
• Updates internal decision signals and routing logic

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Tech Stack

Backend

• FastAPI or Node.js
• Agent orchestration layer
• Asynchronous task execution
• Centralized logging and metrics collection

Frontend

• React or Next.js
• Ticket management dashboard
• Agent decision visualization
• Resolution confidence indicators
• Escalation tracking

Data Layer

• PostgreSQL
  • Tickets
  • Agent decisions
  • Logs and metrics
• Vector Database
  • Semantic memory
  • Contextual retrieval
  • Historical resolution data

Infrastructure

• Dockerized services
• Cloud deployment (AWS or GCP)
• Auto-scaling for traffic spikes
• Cost and latency monitoring

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Ticket Processing Flow

1. User submits a support ticket
2. Classifier Agent analyzes intent and urgency
3. Planner Agent selects an action
4. Resolution Agent attempts solution if applicable
5. Verification Agent validates the outcome
6. Ticket is closed or escalated
7. Learning Agent updates system memory

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Metrics and Observability

The system tracks the following metrics per ticket and in aggregate:

• Ticket resolution rate
• Human escalation rate
• Average resolution time
• Cost per ticket
• Error recovery success rate
• Agent confidence versus outcome accuracy

These metrics enable performance analysis, optimization, and system tuning.

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Project Status

Active development.
Designed for extensibility, evaluation, and real-world deployment.