"Take the example of workflow automation software company Pegasystems, which recently celebrated a financial turnaround on the back of its switch from process automation to so-called agentic AI, causing third-quarter revenue to rise 17 percent from the previous year. "In applications, Pegasystems is working with us to take legacy applications which may have run on-prem or in a hybrid environment and modernize them in the cloud," he tells The Register. The vision is that legacy applications can be combined with newer elements and sewn together with LLM agents to present the end user with a coherent experience. Although AWS doesn't provide applications or LLMs, it is building infrastructure and tooling to support this. Under the hood, Pegasystems uses AWS Bedrock, a managed service offering a choice of foundational models through a single API."
"The model sees the creation of a big ecosystem with a broad spectrum of pondlife. Enterprise application providers - both SaaS and legacy - will rub shoulders with LLM providers and companies promising nebulous concepts like automation, workflow, and a seamless experience."
"On the one hand, Oladehin sees this simplifying application integration with a set of open agent standards, including the Model Context Protocol (MCP) and Agent-to-Agent (A2A). "We used to integrate systems in four or five different ways: through transferring files, through API calls, through SOAP and REST," he says. "You're creating this complexity for customers, and what we've done with a few standards is make it understandable and reduce the complexity.""
Agentic AI adoption is driving enterprises to modernize legacy applications in the cloud and combine legacy elements with LLM agents to deliver coherent end-user experiences. Companies like Pegasystems saw revenue gains after shifting from process automation to agentic AI, using managed services such as AWS Bedrock for foundational models. The resulting ecosystem will include SaaS providers, legacy vendors, LLM providers and many niche players. Open agent standards like MCP and A2A can simplify integration by reducing heterogeneous integration methods. Commercial models remain unresolved as vendors experiment with consumption- and outcome-based licensing and pricing.
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