The standardization and open collaboration of the agent ecosystem is no longer a luxury, but the critical watershed for whether AI Native can be engineered and implemented.

• The establishment of AAIF (Agentic AI Foundation) is the result of leading vendors staking out the “agent protocol layer” in advance.
• The real challenge is not technical, but how organizations transition from “human execution + AI assistance” to “agent execution + human supervision”.
• Successful agent adoption requires a phased adoption path, not just a bunch of protocols and demos.
• CNCF and AAIF are complementary: CNCF manages “what infrastructure agents run on”, AAIF manages “how agents collaborate”. This matches the system I am building in ArkSphere.

Cloud Native Problems Are Solved, AI Native Problems Are Just Beginning

Over the past decade, Cloud Native technologies like Kubernetes, Service Mesh, and microservices have standardized “how applications run in the cloud”. But AI Native faces a completely different challenge: It’s not about “how to deploy a service”, but “how many behaviors in the system can be handed over to agents to execute themselves”.

CNCF’s Cloud Native AI (CNAI) addresses infrastructure-level issues: “How can model training/inference/RAG run at scale and securely on Kubernetes?”

But what AI Native truly lacks is another layer: How do agents collaborate, access tools, get governed, and audited?

This is exactly the gap AAIF aims to fill.

AAIF’s Three Weapons: Protocol + Runtime + Development Standard

AAIF hosts three core technologies contributed by its founding members:

① Anthropic’s Model Context Protocol (MCP)

https://github.com/modelcontextprotocol

A “system call interface for agents”:

• Unified definition for how agents access databases, APIs, files, and external tools.
• Designed to be more like an AI version of gRPC + OAuth.
• Already integrated by Claude, Cursor, ChatGPT, VS Code, Microsoft Copilot, Gemini, and others.

It may not be the flashiest technology, but it could become the plumbing for the entire Agentic ecosystem.

② Block’s Goose Framework

https://github.com/block/goose

Reference runtime for MCP:

• Local-first, composable agent workflow engine.
• Enables enterprises to pilot agents in small scopes without betting on a specific vendor.
• Serves as an engineering template for protocol implementation.

③ OpenAI’s AGENTS.md

https://agents.md

A simple but effective standard:

• Place an AGENTS.md file in the project repository.
• Clearly document build steps, testing, constraints, and context rules.
• Any agent that understands AGENTS.md can operate the codebase using the same instructions.

This makes agent behavior more predictable and auditable.

Why Is AAIF in Such a Hurry? This Is a Race for Standards

Let’s compare with history:

• Kubernetes’ predecessor Borg ran internally at Google for over a decade; K8s was open sourced and donated to CNCF two years later.
• PyTorch joined the Linux Foundation six years after its release.
• MCP was donated to AAIF just over one year after its launch.

AAIF is not about “mature technology entering a foundation”, but staking out the key position early.

The reasons are practical:

1. Prevent agent ecosystem fragmentation Today, there are many competing “tool invocation protocols”, which could become incompatible silos in three years.
2. Protocol layer is easier to reach global consensus than model layer Model competition is inevitable, but protocols can be standardized, open sourced, and avoid vendor lock-in.
3. A necessary move in global tech competition Putting the foundational standards for Agentic AI into the Linux Foundation is both a gesture of cooperation and a strategic move.

AAIF vs CNCF: Not Competition, But Two Pieces of the Puzzle

CNCF’s role:

“What infrastructure do agent workloads run on?” Kubernetes, Service Mesh, observability, AI Gateway, RAG Infra—all at this layer.

AAIF’s role:

“How do agents collaborate, invoke tools, and get governed?” Protocols, runtimes, and behavioral standards—all at this layer.

Analogy:

This matches the upper semantic and lower infrastructure layers in my ArkSphere architecture diagram.

In the long run, the two sides will be tightly coupled: CNCF’s KServe, KAgent, and AI Gateway will natively support MCP / AGENTS.md, AAIF’s Runtime will run on Cloud Native infrastructure by default.

The Real Challenge: Not Protocols, But Organizations and People

Most enterprises will get stuck on:

• How much responsibility can agents actually take?
• Who is accountable when things go wrong?
• How are audit, SLOs, and compliance defined?
• How is multi-agent collaboration visualized?
• How are tool invocation permissions controlled?

In other words, agent adoption is not a “technical migration”, but an “organizational migration”.

If AAIF cannot provide:

• Phased adoption methodologies
• Typical organizational migration paths
• Engineering best practices
• Failure cases and anti-patterns

It will be difficult for AAIF to achieve the industry impact that CNCF did.

Summary: AAIF Is the Moment When Boundaries Are Drawn

For me, the establishment of AAIF feels like:

“The battlefield boundaries of the agent world have finally been drawn. Now it’s up to the engineering community to make it work.”

CNCF solved “how to run Cloud Native”, AAIF is now trying to solve “how agents collaborate”.

In the next five years, whoever can truly connect these two worlds will stand at the gateway to the next generation of infrastructure.

That’s why I started a dedicated “Agentic Runtime + AI Native Infra” research track in ArkSphere.

The ‘Three-Body’ Architecture of the AI Native Era

Finally, a personal note—my thoughts on ArkSphere.

This diagram shows the three-layer structure of the AI Native era:

• CNCF (bottom layer): Provides the Cloud Native foundation required for agent operation, including Kubernetes, Service Mesh, GPU scheduling, and security systems.

• AAIF (middle layer): Defines the runtime semantics and standards for agents, including the MCP protocol, Goose reference runtime, and AGENTS.md behavioral standard.

• ArkSphere (bridging layer): Aligns the “Agentic Runtime semantic layer” with the “AI Native Infra infrastructure layer”, forming an engineerable agent architecture standard.

In short:

Infra is responsible for “running”, Runtime for “how to act”, and ArkSphere for “how to assemble a system”.