TLDR: Amazon Web Services (AWS) has issued a caution regarding the emerging landscape of AI agents, predicting a significant increase in complexity for software pricing and commercial models. While AI agents promise to streamline application integration through new standards, the challenge lies in establishing standardized and transparent pricing structures as vendors grapple with consumption and outcome-based models.
The rapid proliferation of agentic AI solutions is poised to introduce considerable complexity into the software market, particularly concerning deployment and commercial models, according to a recent warning from Amazon Web Services (AWS). Olawale Oladehin, AWS director of solutions architecture, highlighted that standard practices for pricing in this evolving environment are yet to be established. ‘As agentic AI solutions flood the market, users will face a complex environment in terms of deployment and commercial models, with standard practices yet to be resolved,’ Oladehin stated in an interview with The Register on October 27, 2025.
This shift is already evident in the industry. For instance, workflow automation software company Pegasystems recently reported a financial resurgence, with its third-quarter revenue climbing 17 percent year-over-year, attributed to its transition from traditional process automation to agentic AI. Pegasystems leverages AWS Bedrock, a managed service that provides access to various foundational models through a single API, illustrating how AWS is building the underlying infrastructure and tooling to support this burgeoning ecosystem.
Oladehin noted a dichotomy in the impact of AI agents. On one hand, open agent standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) are simplifying application integration. He explained, ‘We used to integrate systems in four or five different ways: through transferring files, through API calls, through SOAP and REST. You’re creating this complexity for customers, and what we’ve done with a few standards is make it understandable and reduce the complexity.’ The Model Context Protocol (MCP), introduced by Anthropic in late 2024 and now adopted by major players like OpenAI and Google, is emerging as a universal interface for AI models, akin to a ‘USB-C port for AI.’
However, this simplification does not extend to commercial arrangements. Vendors are currently grappling with the challenge of developing new licensing models, moving beyond traditional structures to incorporate consumption and outcome-based pricing. This creates a nuanced conversation, as Oladehin described it, with much more ‘dimensionality and choice’ for customers, albeit in ‘early days.’
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Looking ahead, AI agents are expected to offer more flexibility for managing legacy applications. Industry estimates from Gartner suggest that over 60 percent of SAP customers still operate on ECC 6 on-premises, with projections indicating that four in ten ECC customers today will still be using it in key business areas by 2030. The rise of agentic AI could provide innovative solutions for modernizing and integrating these enduring systems, even as the commercial frameworks catch up to the technological advancements.
