The core needs of AI agents—version control, testing, observability—mirror those of human developers. However, the sheer scale and speed of agentic workflows mean existing tools like Kubernetes are insufficient, requiring a fundamental reimagining of the entire infrastructure stack.

MCP shouldn't be thought of as just another developer API like REST. Its true purpose is to enable seamless, consumer-focused pluggability. In a successful future, a user's mom wouldn't know what MCP is; her AI application would just connect to the right services automatically to get tasks done.

Building a single AI tool is not enough. The real value lies in becoming the 'conductor,' creating a system that orchestrates multiple specialized AI agents to complete complex workflows. Whoever owns this coordination layer owns the entire value flow.

Instead of reinventing the wheel, the Toolhive project repurposes battle-tested cloud-native technologies. It packages MCP servers into standard OCI container images, allowing enterprises to use their existing security scanning, hardening, and deployment pipelines for AI infrastructure.

The future of computing isn't programmatic execution but defining high-level objectives. An AI "OS" will orchestrate underlying tools (file systems, code sandboxes, APIs) to achieve a goal, like "build a website that tracks podcast stock mentions." The user interacts with objectives, not commands.

Enterprises will shift from relying on a single large language model to using orchestration platforms. These platforms will allow them to 'hot swap' various models—including smaller, specialized ones—for different tasks within a single system, optimizing for performance, cost, and use case without being locked into one provider.

The technical term "MCP" (Model Component Provider) is confusing. It's simpler and more accurate to think of them as connectors that give AI tools access to knowledge within your other apps and the ability to perform actions in them.

Despite technical debates about bloat, MCPs (Model-Component Packages) serve a crucial strategic role as the "third-party apps" for AI platforms like OpenAI and Anthropic. They provide a vital distribution layer for new products to enter the ecosystem, similar to the App Store.

The manual management of deployment and monitoring will become obsolete. A new, fully AI-managed stack will emerge, allowing founders to simply ask an agent to build and iterate on products. The company's main communication tool may even become the interface for managing these agents.