en.Wedoany.com Reported - Workflow automation vendor Pegasystems (Pega) has released a series of artificial intelligence enhancements aimed at helping enterprises deploy AI agents into critical business processes while maintaining governance, reliability, and cost control.
Announced at the PegaWorld conference, these updates cover agent orchestration, application development, employee training, and a new pricing model to address growing concerns over the cost of large language model-based AI. The announcements are part of the Pega Infinity '26 release, expected to launch in the third quarter.
Pega is tackling an increasingly heated debate: how organizations can scale AI agents beyond pilot projects without incurring operational, compliance, or financial risks. Kerim Akgonul, the company's Chief Product Officer, stated that Infinity '26 may be its most ambitious product launch in over a decade.
Addressing the gap between AI investment and actual returns, Don Schuerman, Pega's CTO and Head of Marketing, noted that people are realizing agents can consume a large number of tokens without substantially improving business efficiency if not careful. Tokens are the data building blocks (such as word parts and punctuation) that AI models process and generate, and most AI model providers charge based on token usage.
At the core of the announcements is expanded support for the Model Context Protocol (MCP), which allows third-party AI agents to discover and execute Pega workflows. Agents built on platforms such as Anthropic's Claude, Google's Gemini, OpenAI's LLM, and Amazon Web Services' AgentCore will be able to invoke Pega-managed business processes while adhering to enterprise governance controls.
Pega believes many agentic AI approaches rely too heavily on repeated reasoning by large language models, leading to inconsistent results and high costs. The company is promoting its so-called "predictable AI" architecture, shifting most AI reasoning to application design time rather than runtime. Kerim Akgonul stated that the new MCP capabilities provide organizations with a simple way to connect their AI agents to critical business processes, orchestrating predictable outcomes at predictable costs.
The company also introduced new agent services, including an "agent assignment broker" that can automatically contact employees or customers when approvals or additional information are needed, and a document processing agent capable of analyzing, classifying, and extracting information from documents, images, and PDFs.
On the development front, Pega launched Infinity Studio, a redesigned, AI-driven development environment that integrates the capabilities of its Blueprint AI workflow design platform. Infinity Studio integrates with third-party coding assistants such as GitHub Copilot, Claude Code, and OpenAI Codex, while embedding best architectural practices into the development process. Developers can use AI assistants within Infinity Studio to configure integrations, design workflows, and modify user interfaces through natural language instructions. The platform automatically generates implementation plans based on Blueprint designs and exposes workflows via the MCP interface.
Pega also announced the "Solution Designer Program," a training and certification initiative aimed at bridging the gap between business needs and technical implementation. The program includes free certification through Pega Academy, Blueprint delivery workshops, and a community project. Early customer reports indicate that its Blueprint Delivered approach increases discovery speed by 50%, with 80% of projects going live within 90 days and a 30% reduction in rework after initial design.
A unique aspect of today's announcement is Pega's abandonment of token-based AI pricing. Under the new model, customers will pay a fixed fee for each completed business case, rather than paying per token consumed. Pega says this approach aims to eliminate the so-called "AI token tax" by reducing reliance on repeated runtime reasoning. The company estimates that some customers could reduce AI costs by more than 20 times, depending on workflow complexity and scale.
Founder and CEO Alan Trefler stated that enterprises are quickly realizing tokenmaxxing is absurd, leading only to unsustainable costs and unpredictable outcomes. Tokenmaxxing is a practice of measuring employee productivity by the number of AI tokens consumed. AI creates the most value when it delivers reliable results at scale. That's why Pega doesn't charge based on the number of tokens customers use, but on the meaningful work they complete. Trefler also emphasized that Pega differs from competitors heavily reliant on prompt-driven agent frameworks, emphasizing deterministic workflows over large numbers of autonomous agents. He believes the key is that every application can have an automatically built agent, and that agent knows how to handle that application's workflows.
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