CORE CONCEPTS

Core Concepts: The Building Blocks of Wamino AI Employees

Understanding these six concepts is key to unlocking the full potential of your AI Employees.

Illustrative workflows and outcomes, not measured customer results or performance guarantees. Available actions depend on the connected systems and permissions you configure.

Task

The atomic unit of AI work

A Task is an individual action that an AI Employee performs — sending an email, creating a purchase order, calling a customer, or updating a record. Tasks are the building blocks that combine into routines and procedures.

Real-World Examples

Task — Real-World Examples
Scenario	What Happens	Outcome
Collections	Send payment reminder email to overdue account	Customer receives personalized follow-up
Sales	Generate and send revised quote with 5% discount	Buyer receives updated proposal in minutes
Purchasing	Create purchase order for items below reorder point	PO sent to preferred vendor automatically
Support	Check order status and compose response to customer inquiry	Customer gets accurate answer in 30 seconds

Single action block: Input → Process → Output

Routine

Automated workflows that run like clockwork

A Routine is a recurring automated workflow that an AI Employee executes on a schedule. Morning AR checks, daily quote follow-ups, weekly inventory reports — routines ensure nothing falls through the cracks.

Real-World Examples

Routine — Real-World Examples
Scenario	What Happens	Outcome
Morning AR Check	Pull overdue invoices, prioritize, send follow-ups	All overdue accounts contacted before 9 AM
Daily Sales Follow-up	Check stale quotes, send reminders, update CRM	No quote goes cold for more than 3 days
Weekly Inventory Report	Scan stock levels, flag low items, generate report	Management gets actionable inventory insights every Monday
End-of-Day Recap	Summarize actions taken, decisions made, escalations pending	Team has full visibility into AI Employee activity

Timeline: 8AM → 10AM → 2PM → 5PM with task blocks

Schedule

When AI work happens

A Schedule defines when and how often an AI Employee's routines and tasks execute. Schedules can be time-based (daily at 8 AM), event-triggered (when a new order arrives), or condition-based (when inventory drops below threshold).

Real-World Examples

Schedule — Real-World Examples
Scenario	What Happens	Outcome
Time-based	Run AR collection routine every weekday at 8:00 AM	Consistent daily execution without human intervention
Event-triggered	Process new order when Shopify webhook fires	Instant order handling, zero delay
Condition-based	Alert when inventory falls below reorder point	Proactive restocking before stockouts
Interval	Check for stale quotes every 4 hours	Continuous pipeline monitoring throughout the day

Calendar grid with trigger icons

Procedure

Business processes with built-in intelligence

A Procedure is a step-by-step business process that includes decision points, conditional branches, and escalation rules. Unlike rigid automations, procedures allow AI Employees to adapt based on context — following the right path for each situation.

Real-World Examples

Procedure — Real-World Examples
Scenario	What Happens	Outcome
Quote Approval	If discount < 5%, auto-approve. If 5-15%, request manager review. If > 15%, escalate to VP.	Right approval level for every deal
Customer Complaint	Assess severity → Check history → Resolve or escalate → Follow up	Consistent handling with appropriate escalation
Vendor Payment	Verify invoice → Match PO → Check terms → Process or flag discrepancy	Accurate payments with built-in fraud prevention
New Hire Onboarding	Create accounts → Assign training → Schedule check-ins → Report completion	No onboarding steps missed

Flowchart with decision diamonds and action blocks

Goal

Measurable outcomes that drive AI behavior

A Goal is a measurable objective that shapes how an AI Employee prioritizes and executes work. Goals like 'reduce overdue AR by 30%' or 'respond to all inquiries within 5 minutes' give AI Employees purpose and allow you to track their performance.

Real-World Examples

Goal — Real-World Examples
Scenario	What Happens	Outcome
Collections	Reduce overdue AR by 30% this quarter	AI prioritizes highest-impact accounts first
Sales	Convert 80% of quotes within 7 days	AI follows up more aggressively on aging quotes
Support	Maintain < 2 minute average response time	AI prioritizes real-time channels during peak hours
Operations	Zero stockouts this month	AI monitors inventory more frequently as levels drop

Dashboard with progress gauges and KPI metrics

Experience

Self-learning expertise from real conversations

An Experience is crystallized knowledge that an AI Employee builds over time by processing real conversations. Cases are automatically extracted from interactions, clustered by scenario type, and distilled into reusable expertise guides — complete with confidence scores, resolution steps, and edge cases. New AI Employees can bootstrap with pre-seeded experiences, while seasoned ones continuously refine their knowledge base.

Real-World Examples

Experience — Real-World Examples
Scenario	What Happens	Outcome
Customer Returns	After resolving 50+ return cases, AI crystallizes a guide covering common return reasons, optimal resolution paths, and edge cases like warranty disputes	New return inquiries resolved 3x faster with 95% satisfaction
Pricing Negotiations	AI distills patterns from 200+ quote negotiations into expertise on discount thresholds, competitor pricing responses, and volume deal structures	Sales AI closes deals faster with proven negotiation strategies
Technical Troubleshooting	AI clusters support tickets by failure type, builds resolution trees with success rates, and identifies recurring root causes	First-contact resolution rate jumps from 60% to 85%
Vendor Onboarding	AI accumulates best practices from onboarding 30+ vendors — typical delays, required documents, common compliance issues	New vendor setup time reduced from 2 weeks to 3 days

Growth timeline: AI Employee gets smarter with every conversation

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