Pega Training

What You'll Learn::

What is Pega? The Enterprise Transformation Company and its mission

Pega Infinity '24 — platform overview, capabilities, and 2026 features

Core Pega products: Pega Platform, Customer Decision Hub, Customer Service, CLM, RPA

Pega's customer base — industries and use cases: banking, insurance, healthcare, telecom, government

The Situational Layer Cake — Pega's reusability and inheritance architecture

PegaWorld 2026 — Blueprint Vibe Coding and platform direction

Career pathways: System Architect, Business Architect, Decisioning Consultant, CLSA

What You'll Learn: :

What is a Rule in Pega? The foundational concept behind all development

Rule types: Case Types, Flows, UI Forms, Decision Rules, Data Objects, Integrations

The Class hierarchy — Base, Abstract, and Concrete classes

Inheritance in Pega — how rules resolve up the class hierarchy

The Situational Layer Cake: Enterprise, Division, Unit, and Application layers

RuleSet Stacks — how Pega finds and applies the correct version of a rule

Versioning and locking — change management in multi-developer environments

What You'll Learn:

App Studio — the low-code business-user-friendly development environment

Dev Studio — the full-featured environment for system architects

When to use App Studio vs. Dev Studio

Dev Studio features: rule forms, class explorer, clipboard, tracer, and performance tools

What is an Application in Pega? Components, channels, and organizational structure

Case Types — the core unit of work in Pega applications

Case hierarchies — parent cases, child cases, and case relationships

Application overlay — extending built-in frameworks and industry templates

What You'll Learn :

Business Process Management principles, goals, and Pega's approach

Straight-through processing (STP) vs. human-in-the-loop workflows

SLAs and escalation in business process management

Pega's security model — authentication, authorization, and access control

Operators, access groups, and access roles

Privilege rules — defining what users can see and do

Page-level, field-level, and process-level security

ISO/IEC 42001 AI management certification and Pega Cloud security

What You'll Learn:

The case life cycle — modeling real business work

Stages, Processes, and Steps — the three tiers of case design

Alternate stages — handling exceptions, cancellations, and special paths

Stage-skipping logic and case-wide processes

Designing life cycles for real-world scenarios: loan processing, insurance claims, HR cases

What You'll Learn:

Assignments — human tasks within case workflows

Router types: Work Queue, Specific Operator, Reporting Manager, Expression-based

Workbaskets vs. worklists — team queues vs. individual inboxes

SLA rule types: Assignment SLAs, Case SLAs, and Custom SLAs

SLA intervals — Goal, Deadline, Passed Deadline, and Overdue

Escalation actions — email notifications and cascading escalations

Reporting on SLA compliance

What You'll Learn:

Flow rules — the graphical automation backbone of Pega processes

Flow shapes: Assignments, Decision shapes, Utility shapes, Connectors, Spinoffs

Spinoffs — launching parallel subprocesses while the main flow continues

Flow actions — what users do within assignments

Connector rules — calling subflows and sub-processes modularly

Best practices for flow design — readability, maintainability, and testing

What You'll Learn:

Approval patterns — single-level, multi-level, and conditional approvals

Authority Matrix — dynamic approval routing based on case data

Parallel approvals and approval delegation

Audit trails — capturing the full approval history

Correspondence rules — automated communication via email, SMS, push

Pega Pulse — in-platform collaboration and case notifications

Multi-language correspondence for global deployments

What You'll Learn:

Parent-child case hierarchies for complex multi-case scenarios

Case sharing and case locking — preventing conflicting simultaneous edits

Cover cases — master case linking related cases

Linked cases — building relationships between independent cases

Optional actions and case-wide actions for flexible workflows

Industry deep dive: insurance claims with sub-cases for investigation, payment, and recovery

What You'll Learn:

Lab: Set up Personal Developer Instance (PDI) via Pega Academy

Lab: Build a full loan application case — stages, processes, assignments, SLA

Lab: Implement a 3-level approval workflow with authority matrix routing

Lab: Configure SLA escalation — email notification and supervisor reassignment

Lab: Build a parent-child case hierarchy — master claim with sub-cases

Lab: Use Blueprint to generate an initial application design from a business description

What You'll Learn :

Data classes vs. work classes in Pega

Properties and their types: Single Value, Page, Page List, Page Group, Value List

The Pega clipboard — how data lives in memory during case processing

Field-level values and data model best practices

Mapping case data to external database structures

What You'll Learn:

What is a Data Page? On-demand, in-memory data loading

Data page scope — Thread, Requestor, and Node-level

Data page source types: Data Transform, Activity, Report Definition, Connector, REST

Refresh strategies and dependent data pages

Data page parameters and caching for high-volume applications

What You'll Learn:

Data Transforms — declarative, visual data manipulation

Setting, appending, removing, and copying property values

Conditional data transforms and default values on case create

Activities — procedural step-based automation for complex logic

Declare Expressions — automatically calculated property values with forward chaining

Declare Triggers and Declare OnChange for event-driven automation

What You'll Learn:

REST Service Rules — configuring outbound REST API calls

SOAP Connector and Service rules — calling WSDL-based web services

Authentication types: Basic, OAuth 2.0, API Key, and JWT

Mapping REST/SOAP responses to Pega data structures

Error handling — timeouts, errors, and retry logic

What You'll Learn:

Report Definitions — querying Pega's database for reporting and lookups

Lookup data patterns — populating dropdown lists and reference data

Database integration via JDBC

File-based integration — reading and writing CSV, XML, JSON

Message-based integration — JMS and Kafka with Pega

Real-time vs. batch integration patterns

What You'll Learn:

Constellation overview — Pega's modern, component-based UI architecture

DX (Digital Experience) API — how the UI layer connects to Pega's backend

Constellation components — the pre-built design system library

View types: Case View, Create View, Review View

Building views in App Studio — templates, layouts, and field configuration

Tabs, accordions, and collapsible sections for complex case UIs

What You'll Learn:

Operator portals — customizing the user workspace in Pega

Navigation pages, My Work list, and home screen configuration

Mashup — embedding Pega case interfaces within external websites

Mobile channel configuration and self-service portals

UI Policies — controlling field visibility, enablement, and required status dynamically

Validation rules — ensuring data quality before case processing continues

Dynamic Select — populating dropdown options based on other field values

What You'll Learn:

Report Definitions — querying and displaying Pega data in lists and summaries

Summary list views — aggregate reports with grouping and totals

Configuring list reports — columns, sorting, filtering, and formatting

Dashboards — configuring operator workspaces with embedded reports

Custom chart types: bar, pie, and bubble charts

Exporting reports — Excel, CSV, and PDF outputs

What You'll Learn:

Lab: Build a data model for an insurance claims application

Lab: Configure a REST connector to call a customer data API and map the response

Lab: Build a data transform to set default values and conditional logic on case create

Lab: Create a Report Definition to query open cases by type and status

Lab: Implement OAuth 2.0 authentication for a REST integration

What You'll Learn:

Lab: Build a case creation view and case review screen in App Studio

Lab: Implement UI policies — conditionally show fields based on case type selection

Lab: Build a validation rule — enforce business rules on form submission

Lab: Configure an operator portal dashboard with embedded work list and charts

Lab: Build a self-service customer portal page for case submission

Lab: Create a summary list report and embed it in a manager dashboard

What You'll Learn:

When rules — the most fundamental decision rule in Pega

Boolean logic: AND, OR, NOT, and comparison operators

Testing When rules — run and preview tools in Dev Studio

Applying When rules in flows, SLAs, UI policies, and routing

Performance best practices for high-frequency evaluation

What You'll Learn:

Decision Tables — matrix-based multi-condition decision logic

Row ordering and conflict resolution

Decision Trees — hierarchical branching decision logic

When to use Decision Tables vs. Decision Trees vs. When rules

Industry examples: loan risk scoring, insurance premium calculation, credit eligibility

What You'll Learn:

Map Value rules — translating one value to another

Pega built-in function library: string, math, date, and collection functions

Custom functions — implementing Java utility functions callable from Pega rules

Scorecard rules — combining multiple weighted factors into a single numeric score

Common scorecard use cases: credit scoring, fraud detection, customer health scoring

Scorecard integration with Customer Decision Hub for AI-enhanced decisioning

What You'll Learn:

Predictive models in Pega — using ML models to predict outcomes

Prediction Studio overview — Pega's AI/ML model management environment

Importing external models (H2O, Python, R, PMML) into Pega

Adaptive models — models that learn and update in real time from every interaction

Propensity scores — probability-based action prioritization

Model monitoring — performance, drift detection, and model refresh

What You'll Learn:

Lab: Build a loan eligibility Decision Table — income, credit score, and loan amount matrix

Lab: Implement a Decision Tree for insurance premium calculation

Lab: Create a fraud risk Scorecard — weighted combination of 6 risk factors

Lab: Configure Declare Expressions for automatic field calculations

Lab: Import a PMML model into Prediction Studio and test propensity scoring

What You'll Learn:

What is Customer Decision Hub? The AI brain for 1:1 customer engagement

The Next-Best-Action (NBA) framework — always-on, inbound, and outbound decisioning

Issues, Groups, and Actions — the CDH taxonomy for organizing offers and actions

Treatments — how actions are delivered across channels

CDH vs. traditional campaign management — always-on vs. batch campaigns

CDH use cases: banking cross-sell, telco churn prevention, insurance retention

What You'll Learn:

Next-Best-Action Designer — guided strategy configuration for business users

Configuring Engagement Policies — eligibility, applicability, and suitability rules

Prioritization and Arbitration — selecting the optimal action for each customer

Business and ethics levers — balancing commercial objectives with customer wellbeing

Strategy components: Filter, Aggregate, Prioritize, Set Property, Group By

Chaining strategies — composing complex decisions from reusable components

What You'll Learn:

How adaptive models power CDH personalization in real time

The Pega learning cycle: present action, customer responds, model updates, better predictions

Adaptive model configuration — learning contexts and response tracking

Always-On Outbound — continuously evaluating which customers should be contacted

Email channel configuration — CDH Email Designer and send scheduling

SMS, push notification, and paid media integration

Volume constraints and frequency caps to protect customer experience

What You'll Learn:

Visual Business Director (VBD) — the marketing analytics workspace in CDH

Analyzing action performance — what is being offered and accepted

Interaction History — the full record of all CDH decisions and customer responses

Decision audit — why a specific action was or was not selected for a customer

A/B testing in CDH — comparing decision strategies and measuring lift

Reporting for business stakeholders — dashboards from Interaction History

What You'll Learn:

Lab: Configure a CDH implementation application — initial setup and channel configuration

Lab: Build an action hierarchy — Issues, Groups, Actions, and Treatments for a telco cross-sell scenario

Lab: Configure engagement policies — eligibility, applicability, and suitability for 3 actions

Lab: Set up NBA Designer — prioritization, arbitration, and ethics and business levers

Lab: Build an outbound email engagement program with CDH Email Designer

Lab: Use Visual Business Director to analyze action performance

What You'll Learn:

Pega Customer Service overview — Forrester Leader in Customer Service Solutions, Q1 2026

Architecture: Interaction Container, Service Case Types, Knowledge Management

Customer context — 360-degree customer view for service agents

Service case type design — capture, resolve, fulfill, and close flows

AI-powered case resolution guidance — Knowledge Buddy for agent assistance

Intelligent case routing based on issue type, customer segment, and agent skill

Integration with Customer Decision Hub for NBA during service interactions

What You'll Learn:

Pega Digital Messaging — unified chat, SMS, and social media interactions

Chatbot configuration — conversational AI for self-service deflection

Voice integration — CTI with screen pop for automatic customer context surfacing

Interaction history — complete cross-channel record for every customer

Knowledge articles — creating, organizing, and maintaining a knowledge base

AI-powered knowledge search — surfacing relevant articles based on case context

Knowledge Buddy — generative AI for instant answers from the knowledge base

What You'll Learn:

Lab: Build a service case type for billing dispute — contact creation to resolution

Lab: Configure omnichannel routing — skill-based routing for voice and chat

Lab: Implement a chatbot for common queries — account balance, payment status, address change

Lab: Configure Knowledge Buddy for AI-powered article suggestion during case work

Lab: Connect CDH to the service interaction — next-best retention offer during service call

Capstone: Present a complete Pega Customer Service implementation design for a telco

What You'll Learn:

What is RPA? Robotic Process Automation concepts and enterprise use cases

Pega's integrated RPA — RPA within the BPM and case management platform

Attended vs. unattended robots — when to use each

Pega Robotic Studio — the development environment for Pega bots

Desktop adapters — connecting bots to Windows, web browsers, and Java apps

Automation building blocks: Get, Set, Click, Invoke, Loop, and Condition actions

Error handling — try/catch, retry logic, and fallback strategies

What You'll Learn:

Browser-based RPA — automating web applications with Pega robots

Dynamic web element identification — handling SPAs and dynamically loaded content

Screen scraping — extracting structured data from web pages

Calling robots from Pega case flows — embedding automation in business processes

Robot task types: Start Task, Monitor Task, and End Task

Attended automation patterns for agent assistance in customer service

Unattended automation patterns for background batch processing

Robot queue management and monitoring dashboards

What You'll Learn:

Lab: Build a desktop bot that reads from CSV and enters data into a legacy Windows application

Lab: Build a web bot that logs into a portal, extracts account data, and updates a Pega case

Lab: Integrate a robot task into a Pega case flow — trigger bot from assignment, map output back to case

Lab: Implement error handling and retry logic

Lab: Configure a robot dashboard — monitor execution, errors, and throughput

What You'll Learn:

Pega Cloud — cloud-native deployment on AWS infrastructure

Pega Cloud tiers, SLAs, availability commitments, and disaster recovery

ISO 9001:2015 and ISO/IEC 42001 certifications for Pega Cloud

RuleSet versioning, locking, and packaging for deployment

Pega Deployment Manager — automated deployment pipelines

Pipeline stages: DEV to QA to UAT to PRODUCTION

Branch development — parallel development by multiple teams

Regional data residency — EU, APAC, and US data sovereignty

What You'll Learn:

Unit testing in Pega — testing individual rules in isolation

PegaUnit — the built-in unit testing framework

Test Automation Framework (TAF) — UI-level automated testing

Running automated regression tests on each deployment

Pega Performance Analyzer (PAL) — identifying performance bottlenecks

Common performance anti-patterns: heavy activities, large clipboard, slow data pages

Caching strategies for high-volume applications

Pega Guardian — automated performance monitoring and alerting

What You'll Learn:

Agile development with Pega — sprint planning and iteration management

Git integration — source control for Pega application artifacts

Jenkins, Azure DevOps, and GitHub Actions integration with Pega pipelines

Application Guardrails — Pega's built-in code quality standards

OWASP Top 10 — securing Pega applications against common vulnerabilities

Pega encryption — data at rest and in transit configuration

GDPR and data privacy compliance — data masking, deletion, and consent

Pega Guardsman security scan — identifying security issues in applications

What You'll Learn:

Lab: Configure a multi-stage deployment pipeline in Pega Deployment Manager

Lab: Write PegaUnit tests for a Decision Table and a data transform

Lab: Run PAL on a case processing flow — identify and fix a performance bottleneck

Lab: Run Pega Guardsman security scan — review and resolve findings

Lab: Configure Application Guardrails and review compliance

Lab: Package an application for production deployment — version, lock, and export RuleSet

What You'll Learn:

Pega GenAI overview — how generative AI is embedded throughout Pega Infinity '24

Pega's AI portfolio: Blueprint, AI Coach, Knowledge Buddy, Prediction Studio, Adaptive Models, CDH

ISO/IEC 42001:2023 certification — Pega's AI management governance framework

Pega's multi-LLM approach — supporting multiple large language models

AI in development: AI Coach for rule guidance, Blueprint for app design

AI in runtime: Knowledge Buddy for agents, CDH for engagement, adaptive models for decisioning

What You'll Learn:

Pega Blueprint overview — the generative AI design and workflow agent

Blueprint capabilities: natural language workflow design, case design, data model, and integration

The Blueprint design canvas — graphical workflow editor powered by AI

Using Blueprint for legacy modernization — uploading documents, code, UI screenshots, and videos

Agentic capabilities in Blueprint — AI agents analyzing legacy assets to generate modernized apps

Blueprint security: regional data residency, client-level file storage, and federated access controls

What You'll Learn:

What is Vibe Coding? The conversational development paradigm in enterprise context

Pega Blueprint Vibe Coding (launched March 5, 2026)

Text and voice interaction with app designs — real-time workflow, data, and logic refinement

Switching between vibe coding and graphical drag-and-drop modeling seamlessly

Combining AI speed with Pega's enterprise governance framework

Accessing Blueprint Vibe Coding via the AI Assistant tab at [pega.com/blueprint](http://pega.com/blueprint)

Real customer results: Proximus (one day to design, four months to production), Vodafone (seven months to one month)

What You'll Learn:

AI Coach — in-platform guidance for business users and developers building rules

AI Coach use cases: suggesting rule configurations, explaining errors, recommending best practices

Knowledge Buddy — generative AI for instant answers from knowledge bases

Knowledge Buddy integration with Pega Customer Service for real-time agent assistance

Knowledge Buddy for self-service — customer-facing conversational knowledge access

AI governance controls — controlling what the AI can and cannot answer

What You'll Learn:

What is Agentic AI in the context of Pega? Autonomous, multi-step reasoning and action

Pega's agentic architecture — how agents perceive, reason, act, and learn

Building agentic workflows in Blueprint — conversational AI designing agentic sequences

Case design for agentic processing — cases where AI drives decisions and actions

Human-in-the-loop controls — when to pause agentic processing for human judgment

Agentic AI in CDH — autonomous decision-making in customer engagement

What You'll Learn:

Pega Client Lifecycle Management (CLM) — advanced agentic AI for client onboarding

AI-powered KYC, document processing, screening, and risk assessment

Moody's partnership — enhanced CLM and Know Your Customer capabilities

Notes to Blueprint (January 2026) — AI-powered legacy Lotus Notes modernization

Industry AI use cases: banking CLM, insurance underwriting, healthcare prior authorization

Pega CLM agentic workflows — autonomous compliance and onboarding processing

What You'll Learn:

Prediction Studio overview — Pega's centralized AI/ML model management environment

Importing and deploying external models: H2O, Python, R, and PMML formats

Adaptive model lifecycle — from initial training to production learning

Model performance monitoring — click-through rates, accept rates, and lift analysis

Prediction sets — grouping predictions for CDH Next-Best-Action strategies

AI model versioning and governance — controlling which models are active in production

What You'll Learn:

Connecting Prediction Studio models directly to CDH decision strategies

Propensity scores in arbitration — how AI probability drives action selection

Combining adaptive models with business eligibility rules for hybrid decisioning

Real-time model updates — how CDH learns from every customer interaction

Context weights — adjusting model sensitivity to different customer signals

AI-driven audience segmentation for outbound engagement programs

What You'll Learn:

Pega's ISO/IEC 42001:2023 AI management certification — detailed breakdown

AI impact assessments — evaluating risk before deploying AI in production

Explainability in Pega AI — explaining model decisions to regulators and customers

Bias monitoring — detecting and mitigating unfair AI outcomes in Pega decisioning

Data privacy and AI — GDPR-compliant AI in CDH and Prediction Studio

Human oversight requirements — when regulators require human approval of AI decisions

EU AI Act implications for Pega customers — high-risk AI use case classification and controls

What You'll Learn:

Lab: Use Blueprint to design a complete banking loan application from a business description document

Lab: Apply Blueprint Vibe Coding — use voice and text to refine workflows and data models conversationally

Lab: Import legacy application screenshots into Blueprint — generate a modernized workflow from legacy UI

Lab: Configure AI Coach for a business user — set up rule guidance for a non-technical product owner

Lab: Deploy Knowledge Buddy for a Pega Customer Service agent — connect knowledge articles and test AI responses

Lab: Build an agentic workflow in Pega — multi-step autonomous case processing with human-in-the-loop

Capstone: Present a Pega AI transformation roadmap — Blueprint, Vibe Coding, CDH, and Agentic AI — for a financial services client