# Section 8: Workflow Diagrams

Section 8: Workflow Diagrams
Elaborated Process Flows & Decision Trees
AI Customer Support Platform


 
8.1 Workflow Overview
This section provides comprehensive documentation of all critical workflows within the AI Customer Support Platform. Each workflow is detailed with step-by-step processes, decision points, system interactions, and error handling procedures.
8.1.1 Workflow Categories
Category
	Workflows Included
	Primary Actors
	Customer Journeys
	New conversation, message flow, session management
	Customer, AI, System
	AI Processing
	Intent recognition, response generation, learning
	AI Engine, Knowledge Base
	Human Handoff
	Escalation, queue management, agent assignment
	AI, Agent, Supervisor
	Agent Operations
	Conversation handling, transfer, resolution
	Agent, Customer, System
	Administrative
	Configuration, reporting, user management
	Admin, Supervisor
	8.1.2 Workflow Notation
Symbol
	Meaning
	Example
	[Actor]
	The entity performing the action
	[Customer], [AI], [Agent]
	→
	Flow direction / leads to
	Step 1 → Step 2
	◆
	Decision point
	◆ Confidence ≥ 70%?
	⚡
	Trigger / Event
	⚡ New message received
	✓
	Success outcome
	✓ Response sent
	✗
	Failure / Error
	✗ Connection failed
	⏱
	Time-based action
	⏱ Wait 30 seconds
	

 
8.2 Customer Message Flow
The Customer Message Flow describes the complete journey of a message from the moment a customer sends it until they receive a response. This is the core workflow of the platform.
8.2.1 Flow Diagram
Step 1
	⚡ TRIGGER
	Customer sends message via chat widget
	Step 2
	RECEIVE
	WebSocket server receives message payload
	Step 3
	VALIDATE
	Validate message format, length, content safety
	Step 4
	PERSIST
	Save message to database with timestamp
	Step 5
	ROUTE
	Determine routing: AI or existing human agent
	Step 6
	PROCESS
	AI Engine analyzes message (or agent receives)
	Step 7
	RESPOND
	Generate and send response to customer
	Step 8
	CONFIRM
	✓ Delivery confirmation via WebSocket
	8.2.2 Detailed Step Breakdown
Step 1: Message Trigger
The workflow begins when a customer submits a message through the chat widget.
Component
	Action
	Data
	Chat Widget
	Capture user input
	Message text, attachments
	Widget JS
	Validate client-side
	Length check, empty check
	WebSocket Client
	Send to server
	JSON payload with metadata
	UI
	Show sending indicator
	Spinner, disabled input
	Message Payload Structure:
JSON Payload
{ conversation_id: 'uuid', content: 'message text', type: 'text', timestamp: 'ISO-8601', metadata: { source: 'widget', device: 'desktop' } }
	Step 2: Message Reception
The server receives and acknowledges the message.
•           WebSocket server validates connection authentication
•           Message is parsed and validated against schema
•           Rate limiting check (max 10 messages per minute)
•           Acknowledgment sent back to client
Step 3: Validation
Validation
	Rule
	On Failure
	Format
	Valid JSON structure
	Reject with error code 400
	Length
	Max 2000 characters
	Truncate or reject
	Content
	No malicious scripts
	Sanitize HTML/JS
	Rate Limit
	≤10 messages/minute
	Queue or reject with 429
	Session
	Valid session token
	Request re-authentication
	Step 4: Persistence
Message is saved to the database for history and analytics.
Field
	Value
	Purpose
	id
	UUID generated
	Unique identifier
	conversation_id
	From payload
	Links to conversation
	sender_type
	'customer'
	Identifies message source
	content
	Sanitized text
	Message body
	created_at
	Server timestamp
	Ordering and analytics
	metadata
	JSON blob
	Device, channel, etc.
	

 
Step 5: Routing Decision
The system determines how to handle the message based on conversation state.
◆ Decision Point: Message Routing
IF conversation.assigned_to IS NOT NULL AND agent.status = 'online' THEN route to Human Agent ELSE route to AI Engine
	Condition
	Route To
	Reason
	New conversation
	AI Engine
	No prior context, start with AI
	AI handling, no escalation
	AI Engine
	Continue AI conversation
	Escalated, agent assigned
	Human Agent
	Agent is handling
	Escalated, no agent yet
	Queue + AI ack
	Waiting for agent
	Agent went offline
	Re-queue + notify
	Reassign needed
	Step 6: AI Processing
When routed to AI, the message goes through the AI Engine pipeline (detailed in Section 8.3).
•           Intent recognition with confidence scoring
•           Entity extraction from message content
•           Context retrieval from conversation history
•           Knowledge base search for relevant articles
•           Response generation using templates and AI
Step 7: Response Delivery
The generated response is sent back to the customer.
Action
	System
	Details
	Format response
	API Server
	Apply template, insert variables
	Save to database
	Database
	Persist for history
	Send via WebSocket
	WS Server
	Real-time delivery
	Fallback to polling
	API Server
	If WebSocket unavailable
	Update UI
	Widget
	Display message bubble
	Step 8: Delivery Confirmation
•           WebSocket acknowledgment confirms message received by client
•           Message status updated to 'delivered' in database
•           Analytics event logged for reporting
•           Typing indicator cleared


 
8.3 AI Processing Pipeline
The AI Processing Pipeline details how the AI Engine analyzes customer messages and generates appropriate responses. This is the intelligence core of the platform.
8.3.1 Pipeline Overview
Stage 1
	PREPROCESS
	Clean and normalize input text
	Stage 2
	INTENT
	Classify user intent with confidence
	Stage 3
	ENTITY
	Extract relevant entities (dates, IDs, etc.)
	Stage 4
	CONTEXT
	Retrieve conversation history and customer data
	Stage 5
	KNOWLEDGE
	Search knowledge base for relevant content
	Stage 6
	DECIDE
	◆ Determine response strategy
	Stage 7
	GENERATE
	Create response from template or AI
	Stage 8
	VALIDATE
	Check response quality and safety
	Stage 9
	OUTPUT
	Return formatted response
	8.3.2 Stage Details
Stage 1: Preprocessing
Raw input is cleaned and normalized for consistent processing.
Operation
	Input
	Output
	Example
	Lowercase
	Mixed case
	All lowercase
	'Hello' → 'hello'
	Trim whitespace
	Extra spaces
	Single spaces
	'hi  there' → 'hi there'
	Remove special chars
	Emojis, symbols
	Clean text
	'Hi!! 😊' → 'Hi'
	Spell correction
	Typos
	Corrected words
	'refudn' → 'refund'
	Language detection
	Any text
	Language code
	'Bonjour' → 'fr'
	Stage 2: Intent Recognition
The system classifies what the customer wants to accomplish.
◆ Decision Point: Intent Confidence
HIGH (≥80%): Direct match, proceed with intent MEDIUM (70-79%): Proceed with clarification option LOW (50-69%): Offer multiple options or retry VERY LOW (<50%): Trigger escalation
	Input
	Processing
	Output
	'Where is my order #12345'
	Pattern matching + ML model
	intent: order_status (92%)
	'I want my money back'
	Semantic analysis
	intent: refund_request (87%)
	'asdfgh'
	No match found
	intent: fallback (100%)
	'Can you help me with billing and also shipping'
	Multi-intent detection
	intents: [billing, shipping]
	Stage 3: Entity Extraction
Relevant data points are extracted from the message.
Entity Type
	Pattern/Method
	Example Extraction
	Order ID
	Regex: #[0-9]+
	'#12345' → order_id: 12345
	Email
	Regex: email pattern
	'john@email.com' → email: john@email.com
	Date
	NLP date parsing
	'next Tuesday' → date: 2025-01-28
	Amount
	Currency pattern
	'$99.99' → amount: 99.99, currency: USD
	Product
	Entity recognition
	'iPhone 15' → product: iPhone 15
	

 
Stage 4: Context Retrieval
Historical context is gathered to inform the response.
•           Last 10 messages from current conversation
•           Previous intents detected in this session
•           Customer profile data (name, email, tier)
•           Previous conversations summary (if returning customer)
•           Any pending actions or follow-ups
Context Item
	Source
	Usage
	Conversation history
	Messages table
	Maintain continuity
	Customer name
	Customers table
	Personalize response
	Customer tier
	Customers table
	Priority routing
	Previous issues
	Conversations table
	Reference past interactions
	Pending orders
	External API
	Proactive information
	Stage 5: Knowledge Base Search
Relevant articles are retrieved to enhance the response.
•           Semantic search using intent and entities as query
•           Top 3 matching articles retrieved
•           Relevance score calculated for each
•           Article content summarized for inclusion
Stage 6: Response Decision
◆ Decision Tree: Response Strategy
IF confidence ≥ 70% AND intent has template:   → Use template response ELSE IF confidence ≥ 70% AND KB match:   → Generate from KB content ELSE IF confidence 50-69% AND retry_count < 2:   → Ask clarifying question ELSE:   → Trigger human escalation
	Stage 7: Response Generation
Method
	When Used
	Example
	Template + Variables
	High confidence, standard query
	'Your order #{{order_id}} is {{status}}'
	KB Summary
	Information request
	'Based on our policy: {{article_summary}}'
	AI Generation
	Complex or novel query
	LLM generates contextual response
	Clarification
	Ambiguous intent
	'Did you mean A or B?'
	Escalation Message
	Low confidence
	'Let me connect you with a specialist'
	Stage 8: Response Validation
•           Check response length (not too short or long)
•           Verify all variables were substituted
•           Content safety scan (no inappropriate content)
•           Grammar and spelling check
•           Tone analysis (professional, helpful)


 
8.4 Human Escalation Workflow
The Human Escalation Workflow manages the transition from AI to human support when automation cannot fully address customer needs.
8.4.1 Escalation Triggers
Trigger ID
	Trigger
	Detection Method
	Priority
	ESC-01
	User explicitly requests human
	Keyword detection
	Critical
	ESC-02
	AI confidence < 50%
	Confidence score
	High
	ESC-03
	Confidence 50-69% twice
	Consecutive check
	High
	ESC-04
	3 consecutive fallbacks
	Fallback counter
	High
	ESC-05
	Negative sentiment
	Sentiment analysis
	Critical
	ESC-06
	Complex multi-intent
	Intent count > 2
	Medium
	ESC-07
	Sensitive topic detected
	Topic classifier
	High
	ESC-08
	Customer is VIP
	Customer tier check
	High
	8.4.2 Escalation Flow
Step 1
	⚡ TRIGGER
	Escalation condition detected
	Step 2
	PREPARE
	Generate AI summary of conversation
	Step 3
	NOTIFY
	Send pre-escalation message to customer
	Step 4
	QUEUE
	Add to escalation queue with priority
	Step 5
	ALERT
	Notify available agents
	Step 6
	ASSIGN
	Agent claims or auto-assigned
	Step 7
	HANDOFF
	Transfer context to agent interface
	Step 8
	CONFIRM
	✓ Agent connected message to customer
	8.4.3 Pre-Escalation Processing
AI Summary Generation
Before escalation, AI generates a summary for the receiving agent.
Summary Component
	Content
	Purpose
	Issue Summary
	1-2 sentence problem description
	Quick understanding
	Detected Intent
	Primary intent and confidence
	Context for agent
	Extracted Entities
	Order IDs, dates, amounts
	Key data points
	Escalation Reason
	Why AI escalated
	Agent awareness
	Suggested Response
	AI-recommended reply
	Speed up resolution
	Related KB Articles
	Top matching articles
	Reference material
	Customer Notification
Customer receives a message indicating the transition.
Pre-Escalation Message Template
"I'd like to connect you with a team member who can better assist with this. Before I do, is there anything else you'd like me to note about your issue?"  [Wait for response or 30 seconds]  "Connecting you now. Estimated wait time: {{wait_time}}. A specialist will be with you shortly."
	

 
8.4.4 Queue Management
Queue Entry
Field
	Value
	Purpose
	conversation_id
	UUID
	Reference to conversation
	priority
	1-5 (1=highest)
	Queue ordering
	escalation_reason
	Trigger code
	Agent context
	customer_tier
	VIP/Regular
	Priority boost
	entered_at
	Timestamp
	Wait time tracking
	sla_deadline
	Timestamp
	SLA monitoring
	Priority Calculation
Priority Formula
Base Priority = Escalation Type Priority + VIP Bonus (if applicable): -1 + Long Wait Penalty: -1 for every 2 minutes waiting + Negative Sentiment: -1  Lower number = Higher priority
	Factor
	Priority Impact
	Example
	User request for human
	Priority 1 (Critical)
	"Talk to a person"
	Negative sentiment
	Priority 1 (Critical)
	Angry customer
	Low AI confidence
	Priority 2 (High)
	Confidence < 50%
	VIP customer
	-1 to base priority
	Enterprise tier
	Wait > 2 minutes
	-1 per 2 min
	4 min wait = -2
	8.4.5 Agent Assignment
Assignment Methods
Method
	Description
	When Used
	Manual Claim
	Agent picks from queue
	Default method
	Round Robin
	System assigns evenly
	High volume periods
	Skill-Based
	Match skills to issue
	Technical issues
	Load-Based
	Assign to least busy
	Balancing workload
	Direct Assignment
	Admin assigns specific agent
	Escalations, VIPs
	Agent Selection Criteria
•           Agent status is 'Online' (not Busy, Away, or Offline)
•           Agent has capacity (below max concurrent chats)
•           Agent has required skills (if skill-based routing)
•           Agent is in correct team/department
•           Preference for agent who handled previous conversations


 
8.5 Agent Conversation Handling
This workflow covers how agents receive, handle, and resolve customer conversations after escalation.
8.5.1 Agent Receives Conversation
Step 1
	⚡ NOTIFY
	Agent receives notification of assignment
	Step 2
	LOAD
	Conversation appears in agent's inbox
	Step 3
	CONTEXT
	AI summary and history displayed
	Step 4
	REVIEW
	Agent reads conversation history
	Step 5
	ENGAGE
	Agent sends first response to customer
	Agent Notification Channels
Channel
	Trigger
	Content
	In-App Badge
	Immediate
	Inbox count increments
	In-App Sound
	Immediate
	Configurable alert sound
	Browser Push
	If tab not focused
	Customer name + preview
	Desktop Alert
	If enabled
	Native OS notification
	Tab Title Flash
	If tab not focused
	Title flashes with count
	8.5.2 Message Handling
Step 1
	RECEIVE
	Customer message appears in real-time
	Step 2
	ANALYZE
	AI suggests responses (optional)
	Step 3
	COMPOSE
	Agent types or selects response
	Step 4
	ENHANCE
	Use canned response or KB article
	Step 5
	SEND
	Response delivered to customer
	Step 6
	LOG
	Message saved with agent attribution
	AI Assistance for Agents
While handling conversations, agents receive AI-powered assistance.
Assistance Type
	Description
	Display Location
	Suggested Responses
	2-3 AI-generated reply options
	Above message input
	Related KB Articles
	Relevant knowledge base links
	Right panel
	Customer Sentiment
	Real-time sentiment indicator
	Conversation header
	Entity Highlights
	Key data highlighted in chat
	In message bubbles
	Quick Actions
	One-click common actions
	Toolbar
	8.5.3 Internal Notes
Agents can add notes visible only to other agents.
Use Case
	Example Note
	Visibility
	Context for transfer
	"Customer is frustrated, handle carefully"
	Receiving agent
	Technical details
	"Checked system - order stuck in processing"
	All agents
	Follow-up reminder
	"Promised callback tomorrow at 2 PM"
	All agents
	Supervisor escalation
	"Need manager approval for refund > $500"
	Supervisors
	

 
8.5.4 Conversation Transfer
Agents can transfer conversations to other agents or teams.
Step 1
	INITIATE
	Agent clicks Transfer button
	Step 2
	SELECT
	Choose agent or team from list
	Step 3
	NOTE
	Add internal note for receiving agent
	Step 4
	CONFIRM
	Confirm transfer action
	Step 5
	NOTIFY
	Customer informed of transfer
	Step 6
	HANDOFF
	Conversation moves to new agent
	Step 7
	COMPLETE
	✓ Original agent released
	Transfer Types
Type
	Recipient
	Use Case
	Agent Transfer
	Specific agent by name
	Specialist needed, shift handoff
	Team Transfer
	Team queue (e.g., Billing)
	Department routing
	Supervisor Escalation
	Team supervisor
	Authority needed
	Return to Queue
	General queue
	Agent unavailable
	8.5.5 Conversation Resolution
Step 1
	INITIATE
	Agent clicks Resolve button
	Step 2
	TYPE
	Select resolution type
	Step 3
	SUMMARY
	Add optional resolution note
	Step 4
	TAGS
	Apply tags for categorization
	Step 5
	MESSAGE
	Send closing message to customer
	Step 6
	SURVEY
	Trigger CSAT survey (if enabled)
	Step 7
	CLOSE
	Update status to Resolved
	Step 8
	METRICS
	Calculate and log handle time
	Resolution Types
Type
	Description
	Customer Message
	CSAT Survey
	Resolved
	Issue successfully fixed
	Closing message sent
	Yes
	Closed
	No action needed/possible
	Brief closing sent
	Optional
	Spam
	Spam or abuse
	No message
	No
	

 
8.6 Customer Satisfaction Survey Flow
After conversation resolution, customers may receive a satisfaction survey to measure service quality.
8.6.1 Survey Trigger
Condition
	Survey Sent
	Reason
	Resolved by AI
	Yes (if enabled)
	Measure AI effectiveness
	Resolved by Agent
	Yes
	Standard feedback
	Closed (no resolution)
	Optional
	Configurable
	Marked as Spam
	No
	Not applicable
	Customer abandoned
	No
	Not completed
	8.6.2 Survey Flow
Step 1
	TRIGGER
	Conversation marked as Resolved
	Step 2
	DELAY
	⏱ Wait 30 seconds
	Step 3
	DISPLAY
	Show rating prompt in widget
	Step 4
	RATE
	Customer selects 1-5 stars
	Step 5
	COMMENT
	Optional text feedback
	Step 6
	SUBMIT
	Save response to database
	Step 7
	THANK
	Display thank you message
	Step 8
	ANALYZE
	Update metrics and reports
	8.6.3 Survey Questions
Question
	Type
	Required
	How satisfied are you with our support?
	1-5 star rating
	Yes
	Was your issue resolved?
	Yes/No/Partially
	Optional
	Any additional feedback?
	Free text (500 chars)
	No
	Would you recommend us?
	0-10 NPS scale
	Optional
	8.6.4 Survey Analysis
•           Individual responses linked to conversation and agent
•           Daily/weekly/monthly aggregation for trends
•           Low score alerts sent to supervisors
•           AI analysis of text feedback for themes
•           Correlation with conversation attributes (wait time, transfers, etc.)


 
8.7 Offline and After-Hours Flow
This workflow handles customer interactions when human agents are unavailable.
8.7.1 Business Hours Check
Scenario
	AI Available
	Humans Available
	Behavior
	Business hours, agents online
	Yes
	Yes
	Normal flow
	Business hours, all agents busy
	Yes
	Queue
	AI + queue with wait time
	After hours
	Yes
	No
	AI only, no escalation option
	Holiday
	Yes
	No
	AI + holiday message
	System maintenance
	Limited
	No
	Maintenance message
	8.7.2 After-Hours Flow
Step 1
	DETECT
	System checks current time against business hours
	Step 2
	NOTIFY
	Widget shows "Outside business hours" banner
	Step 3
	AI ONLY
	AI handles conversation without escalation
	Step 4
	COLLECT
	If AI can't resolve, collect customer details
	Step 5
	PROMISE
	Promise callback/email when agents return
	Step 6
	QUEUE
	Add to next-day queue with priority
	Step 7
	CONFIRM
	Send confirmation email to customer
	8.7.3 Leave Message Flow
When AI cannot resolve and no agents are available, customers can leave a message.
Field
	Required
	Purpose
	Name
	Yes
	Identify customer
	Email
	Yes
	Contact for follow-up
	Phone
	Optional
	Alternative contact
	Issue description
	Yes
	Understand problem
	Preferred callback time
	Optional
	Schedule follow-up
	Leave Message Confirmation
"Thank you for contacting us! We've received your message and a team member will reach out to you at {{email}} within {{sla_hours}} hours. Your reference number is #{{ticket_id}}."
	

 
8.8 Error Handling Workflows
This section documents how the system handles various error conditions to ensure graceful degradation and good user experience.
8.8.1 Connection Errors
Error
	Detection
	User Message
	Recovery
	WebSocket disconnect
	Connection lost event
	"Reconnecting..."
	Auto-retry 3x, then fallback to polling
	Server timeout
	No response in 30s
	"Taking longer than usual..."
	Retry request, show cached data
	Network offline
	Navigator.onLine = false
	"You appear to be offline"
	Queue messages, send when online
	Session expired
	401 response
	"Session expired"
	Prompt re-authentication
	8.8.2 AI Service Errors
Error
	Detection
	Fallback
	Notification
	AI API timeout
	No response in 10s
	Use fallback response
	Alert ops team
	AI API error
	5xx response
	Graceful escalation
	Alert ops team
	Rate limit exceeded
	429 response
	Queue and retry
	Log for capacity planning
	Invalid response
	Parse error
	Use fallback response
	Log for debugging
	8.8.3 Fallback Responses
When AI cannot generate a proper response, fallback messages are used.
Fallback Response Template
"I apologize, but I'm having trouble understanding your request right now. Could you please rephrase your question? Or if you'd prefer, I can connect you with a team member who can help."  [Offer escalation option if within business hours]
	8.8.4 Error Recovery Flow
Step 1
	DETECT
	Error condition identified
	Step 2
	LOG
	Record error details for debugging
	Step 3
	CLASSIFY
	Determine error severity and type
	Step 4
	NOTIFY USER
	Display appropriate user message
	Step 5
	ATTEMPT RECOVERY
	Try automatic recovery if possible
	Step 6
	ESCALATE
	If critical, alert operations team
	Step 7
	FALLBACK
	Use fallback behavior if recovery fails
	Step 8
	MONITOR
	Track for patterns and recurring issues
	

 
8.9 Administrative Workflows
8.9.1 User Management
Create User
	Admin → User Management → Add User → Fill form → Set role → Send invite
	Deactivate User
	Admin → User Management → Select user → Deactivate → Confirm → Sessions invalidated
	Change Role
	Admin → User Management → Select user → Edit → Change role → Save → Permissions updated
	Reset Password
	Admin → User Management → Select user → Reset password → Email sent to user
	8.9.2 Knowledge Base Management
Create Article
	Admin → KB → New Article → Write content → Set category → Preview → Publish
	Edit Article
	Admin → KB → Select article → Edit → Make changes → Save draft or Publish
	Archive Article
	Admin → KB → Select article → Archive → Confirm → Removed from AI search
	Bulk Import
	Admin → KB → Import → Upload CSV → Map fields → Validate → Import
	8.9.3 AI Configuration
Add Intent
	Admin → AI Config → New Intent → Name → Add training phrases → Add responses → Test → Activate
	Edit Intent
	Admin → AI Config → Select intent → Edit phrases/responses → Test → Save
	Train Model
	Admin → AI Config → Training → Review changes → Start training → Monitor progress
	Test Intent
	Admin → AI Config → Test Console → Enter message → View results → Adjust if needed
	8.9.4 Reporting Workflow
Generate Report
	Admin → Reports → Select type → Set date range → Set filters → Generate → View/Export
	Schedule Report
	Admin → Reports → Select type → Schedule → Set frequency → Set recipients → Activate
	Dashboard Review
	Admin → Dashboard → Select date range → Review metrics → Drill down → Take action
	

 
8.10 Workflow Summary
8.10.1 Key Workflows by Module
Module
	Primary Workflows
	Triggers
	Chat Widget
	Session start, message send, reconnection
	User action, system events
	AI Engine
	Processing pipeline, response generation
	Incoming message
	Human Handoff
	Escalation, queue management, assignment
	Escalation triggers
	Agent Interface
	Handle conversation, transfer, resolve
	Agent actions
	Admin Dashboard
	Reporting, configuration, user management
	Admin actions
	8.10.2 Critical Paths
•           Happy Path: Customer → AI → Resolution (target: 70% of conversations)
•           Escalation Path: Customer → AI → Human → Resolution (target: <30%)
•           Error Path: Error → Fallback → Recovery or Graceful Degradation
8.10.3 Performance Targets by Workflow
Workflow
	Target Duration
	SLA
	Message send to AI response
	<2 seconds
	95th percentile
	Escalation trigger to queue entry
	<2 seconds
	99th percentile
	Queue entry to agent assignment
	<2 minutes
	90th percentile
	Agent response to customer
	<1 minute
	85th percentile
	Total conversation resolution
	<10 minutes
	80th percentile
	 
— End of Section 8 —


