A leading enterprise SaaS platform provider, whose portfolio includes a widely used integration platform, watched agentic AI change the question its customers were asking. Enterprises were no longer only connecting applications to each other; they were starting to connect autonomous AI agents to internal systems, data and tools. New integration protocols, most visibly the Model Context Protocol (MCP) and the agent-to-agent (A2A) protocol, promised to standardize that connection.
For the client this was both an opportunity and a threat. If a protocol like MCP became the way enterprises wire agents into their systems, its integration business would need to support it, and possibly be rebuilt around it. If the protocols stalled on security or enterprise readiness, betting early would waste effort. The client needed a clear-eyed view of current adoption, the barriers enterprises were hitting, and which protocols were positioned to last, so it could set its own product direction.
PP&A designed and led an in-depth study across the United States and Europe, built on twelve expert interviews with AI development professionals and decision-makers at enterprise organizations that were actually architecting and deploying generative and agentic AI use cases, in banking, healthcare and life sciences, retail and technology.
The interview guide moved from context to specifics: how mature each organization's agentic AI deployments were, what architectures and tooling they used, how they measured success, which governance and compliance constraints shaped their choices, and then their direct experience with MCP, A2A and the alternatives. Each interview also explored how the participants expected integration platforms to evolve as agents took on more of the workload.
The findings were synthesized into a report structured around four questions: where agentic AI stands today, how enterprises integrate it, which protocols are gaining ground and why, and what role an integration platform can play in an agent-driven world.
The study gave the client a grounded picture rather than a hype cycle. Most enterprises were still in early piloting despite AI-first mandates, deploying hybrid architectures that keep high-risk workloads on self-hosted models, and struggling more with evaluation and governance than with technology. MCP had emerged as the most widely recognized protocol, repeatedly described as a de facto standard with strong reusability benefits, while A2A had far lower practical adoption. At the same time, participants were explicit about MCP's enterprise gaps: security vulnerabilities, scalability limits and missing enterprise-grade features, alongside a shortage of people who know how to build with it.
The most actionable conclusion concerned the client's own platform. Participants saw integration platforms as uniquely placed to solve the complexity of connecting agents to legacy systems and to provide governance as a service, provided they evolve from API management toward AI-native coordination that embraces MCP and agent protocols. That framing, with the segment-by-segment differences in appetite, gave the client a defensible basis for its product roadmap and positioning in the agentic AI market.
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