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The AI landscape is splitting into two camps: those building walled gardens and those building open protocols. If you're betting your business on AI tools, this distinction will determine whether you're building on bedrock or quicksand.
OpenAI's GPT Store launched with fanfare—a curated marketplace of custom GPTs, all locked into their ecosystem. Meanwhile, open protocols like Agent-to-Agent (A2A) and Model Context Protocol (MCP) are quietly building the infrastructure that will make proprietary app stores obsolete.
Here's the uncomfortable truth: interoperability isn't just a feature—it's the moat. And the companies that realize this first will dominate the next decade.
OpenAI's GPT Store feels innovative until you try to leave. Want to use your custom GPT with Claude? Export it to ? Integrate it with your existing toolchain? You can't. You've built a valuable asset on rented land, and the landlord controls the terms.
This isn't just theoretical. We've seen this movie before:
The pattern is predictable: platforms attract builders with openness, then extract value through control once they've achieved critical mass.
Open protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) represent a fundamentally different approach. Instead of trapping capabilities inside a proprietary system, they define how AI systems communicate.
MCP solves a deceptively simple problem: how do AI agents access tools reliably?
Instead of each AI platform building custom integrations for every possible tool (calendar, email, database, API), MCP creates a standardized protocol. Write an MCP server once, and any MCP-compatible agent can use it—whether that's Claude, GPT-4, and other models, or an open-source model.
This is the difference between building a custom electrical outlet for every appliance versus standardizing on 120V AC. The latter creates an entire ecosystem of compatible devices.
At JobInterview.live, our AI Board Room uses MCP extensively. When Atlas (our strategic agent) needs to analyze market data, or when Cipher (our technical architect) needs to access code repositories, they're using MCP servers. The crucial detail? We can swap the underlying model without rewriting integrations.
A2A (Agent-to-Agent protocol) takes this further by standardizing how AI agents work together.
Traditional AI assistants are monolithic—one model trying to be good at everything. The AI Board Room model flips this: specialized agents (Atlas for strategy, Nova for operations, Cipher for technical execution) that delegate to each other based on expertise.
This only works with a shared protocol. A2A defines:
When you ask the AI Board Room to "analyze our competitor's pricing strategy and recommend technical architecture for a new feature," Atlas handles the strategic analysis, then delegates to Cipher via A2A for the technical breakdown. Each agent loads specialized Skills (modular expertise defined via SKILL.md files) and maintains context through the User Dossier.
Here's where this gets interesting for solo founders and small teams.
In the old world, building sophisticated AI systems required:
In the open protocol world, you get:
This is why we built the AI Board Room on open standards. Our Deterministic Backbone (powered by Google ADK) ensures reliable execution, but we're not locked into any single model provider.
Let's get concrete. Here's how open protocols manifest in a real system:
Instead of training custom models or fine-tuning (expensive, fragile, vendor-locked), we define Skills as markdown files. A SKILL.md might contain:
Any agent that can read markdown can load these skills. They're portable, versionable, and human-readable.
When Nova (our operations agent) needs to research market trends, she doesn't have a hardcoded Google Search integration. She uses an MCP server that provides search capabilities. Tomorrow, if we want to swap in a different search provider, we update the MCP server—Nova's code doesn't change.
Our voice mode uses native audio capabilities—not because we're locked into but because it's currently the best option. When someone else builds better audio, we'll swap it. The interface remains consistent.
Here's where quality control meets open architecture. After any AI interaction, our Action Extraction system identifies concrete next steps. Then a Critic Agent reviews the output for quality, accuracy, and completeness.
Crucially, the Critic can be a different model than the primary agent. Maybe GPT-4 generates the strategy, but Claude acts as critic. This multi-model approach is only possible with open protocols.
If you're a solo founder or small team, you can't afford to rebuild your AI infrastructure every time a new model launches. You need strategic flexibility.
Building on open protocols means:
The AI Board Room demonstrates this in practice. Atlas, Cipher, Nova, and the rest aren't just chatbots—they're specialized agents that collaborate via open protocols, maintain context through User Dossiers, and execute reliably through a Deterministic Backbone.
Here's the kicker: every developer who builds an MCP server or A2A-compatible agent makes the entire ecosystem more valuable.
When someone creates an MCP server for Stripe, every agent can suddenly process payments. When someone builds an A2A agent for legal document review, every multi-agent system gains that capability.
This is the opposite of a walled garden, where value accrues only to the platform owner. In open protocol ecosystems, value accrues to everyone.
Within 18 months, proprietary AI app stores will feel as outdated as AOL's walled garden felt after the web emerged.
Not because OpenAI or Anthropic will fail—they're building excellent models. But because the integration layer will move to open protocols, and models will become interchangeable commodities competing on performance and price.
The winners will be those who bet on compatibility over control.
The AI Board Room at JobInterview.live is our stake in the ground: a production system built entirely on open protocols, demonstrating that sophisticated multi-agent systems don't require vendor lock-in.
Try it yourself. Talk to Atlas about your business strategy. Let Cipher architect your technical roadmap. Experience what happens when specialized AI agents collaborate through open standards.
The future isn't about which AI assistant you choose. It's about building systems where you never have to choose at all.
Ready to experience the interoperability advantage? Visit JobInterview.live and meet your AI Board Room.
