Pegasystems launches Pega Infinity '25 platform with AI agents

Pegasystems Inc. (NASDAQ: PEGA) announced the general availability of Pega Infinity '25, which the company describes as an agentic enterprise transformation platform. The platform combines artificial intelligence capabilities for workflow design and runtime execution to help organizations modernize legacy systems.

The platform includes Pega Blueprint, which uses AI agents to analyze legacy system assets including documents, process diagrams, data files, and source code. These agents extract insights and identify case types, data models, and integrations needed to build new workflow applications. Organizations can create versioned snapshots of their Blueprint designs for future reference.

Pega Infinity '25 incorporates design agents that propose workflows across business domains, and runtime agents that use semantic AI to execute specific workflow steps. The system includes audit trails and compliance tracking for enterprise governance requirements.

The platform integrates task mining and process mining capabilities to provide analysis of desktop-level activities and system-wide business processes. New cloud data migration features include auto-generated data models and native connections for AWS Redshift and Google BigQuery.

Updates to Pega's existing solutions include enhanced email authoring tools in Customer Decision Hub, AI-powered self-service agents in Customer Service, and performance tracking features. Sales Automation receives an AI assistant for action prioritization and Microsoft Teams integration.

Pega Infinity '25 is available for existing Pega Cloud customers, according to the company's press release. The Pega Agentic Process Fabric component will be released next month.

"With Pega Infinity, we've taken a different approach," said Kerim Akgonul, chief product officer at Pega. "Instead of introducing unpredictable agents into mission-critical environments, we've built the first platform that gets agents right, combining creative AI at design time with predictable execution at runtime."

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