Pegasystems vs Writer

Pega's real-time decisioning engine for marketing and customer experience teams: it picks the next best action for each customer across web, mobile, email, in-store and phone, and uses GenAI to adjust content and messaging as it runs. One of the products in the Pega Infinity portfolio.

A generative-AI tool for marketing and CX teams to design customer journeys and preview AI-driven personalization strategies, whose output can be imported into Pega Customer Decision Hub.

Automates customer-service journeys using AI-driven workflow automation and robotic process automation.

An always-on generative-AI mentor embedded in Pega workflows that analyzes in-progress work and gives step-by-step guidance to help users complete cases correctly.

An AI knowledge assistant that connects to existing enterprise content stores and answers questions from customers and employees across customer-facing and employee-facing applications.

Applies real-time predictive AI and event processing to running workflows to predict and prevent SLA breaches, address service delays proactively, and route work more effectively.

An orchestration layer that registers AI agents, workflows and data from Pega and third-party systems into a unified directory and routes user requests to the right agent or workflow, with support for MCP and agent-to-agent communication.

Pega's platform for orchestrating agents, people and systems on the same work: Blueprint generates the workflow design, Pega GenAI runs inside it, and the decisioning engine the company has sold for four decades is what the agents act on.

Universal context layer that grounds AI agents in a company's Agent Memory (captured decisions and preferences) and Company Standards (brand, tone, compliance), listed in WRITER's own top-level product navigation alongside Models, AI Studio, WRITER Agent and WRITER for Slack.

Reusable, repeatable workflows in WRITER, so a piece of work done once can be run the same way again.

WRITER's build surface: business teams assemble agents on an interoperable platform while technology teams keep visibility and governance over what ships.

WRITER's retrieval layer. A specialised LLM reads a customer's own documents, spreadsheets, slides and PDFs and builds a graph of the semantic relationships between them rather than storing vector embeddings, so retrieval can follow multi-hop connections, break a broad question into sub-questions and return specific source citations before WRITER's Palmyra models generate the answer.

Enterprise platform for building AI applications and agents on Writer's own models.