AI model providers are increasingly moving up the stack, reshaping the technology ecosystem and redefining questions of sovereignty and control. This shift is evident in recent developments from leaders like Anthropic, OpenAI, Google, and Microsoft, who are no longer just offering foundational models but building comprehensive agentic systems that handle complex workflows autonomously. The core driver behind this evolution is the growing maturity of AI reasoning and coding capabilities. With models now capable of understanding context, planning multi-step actions, and executing tasks across tools, the focus has shifted from raw model performance to intelligent orchestration. Anthropic’s recent emphasis on “Skills” rather than agents reflects this trend—positioning AI as a set of reusable, modular capabilities that can be composed into larger systems, rather than monolithic agents. Three distinct AI agent architectures are emerging. The first involves simple, rule-based agents for narrow tasks. The second integrates models with APIs and workflows for semi-autonomous execution. The third, exemplified by Anthropic’s approach, leverages advanced reasoning and code generation to enable agents that can autonomously design, debug, and deploy applications—what we might call “self-improving” systems. This architecture is particularly powerful when paired with models like Claude Code, which can write, test, and refine software with minimal human input. This upward movement is being formalized through industry-wide collaboration. In late 2025, OpenAI, Anthropic, Google, and Microsoft launched the Agentic AI Foundation under the Linux Foundation to standardize open protocols. Key contributions include Anthropic’s Model Context Protocol (MCP), which enables seamless tool integration; OpenAI’s AGENTS.md and Computer-Using Agent (CUA) frameworks; and Google’s Agent-to-Agent (A2A) communication standards. These protocols are accelerating interoperability and adoption, with over 60,000 projects adopting MCP since August 2025. In consumer AI, this shift is making systems more proactive and embedded. Instead of users initiating every interaction, AI agents now anticipate needs, manage tasks across apps, and operate in ambient computing environments. OpenAI’s Agent Mode and Google’s Gemini integrations are examples of this trend, reducing dependency on third-party applications and creating more seamless, personalized experiences. In enterprise settings, the move up the stack is driving scalable, domain-specific workflows. By 2026, AI agents are expected to manage compliance checks, revenue operations, supply chain logistics, and more. With tools like MCP enabling agent interoperability, organizations can assign P&L ownership to AI systems, marking a shift from experimentation to industrialization. However, this vertical integration raises concerns about control and sovereignty. As model providers own more of the stack—from training to deployment—organizations risk vendor lock-in and reduced transparency. With 72% of enterprise leaders identifying data and model sovereignty as their top challenge in 2026, many are pursuing independent data planes and private AI instances. This tension is fueling demand for domain-specific agents, specialized integrators, and open standards. While giants expand their ecosystems, opportunities remain for niche players who can deliver transparency, auditability, and tailored solutions. Ultimately, the AI stack is no longer just about models. It’s about intelligence, autonomy, and control. As providers move up the stack, the battle for value capture intensifies—but so does the opportunity for innovation, sovereignty, and specialization.