Enterprise applications don’t fail because they lack features. They fail because their architectures can’t adapt fast enough.
I recently spoke with a marketing director who had invested heavily in a legacy cloud platform, only for it to become obsolete within months. Her story illustrates a broader problem facing developers today: as AI standards evolve weekly, most enterprise architectures can’t keep up, trapped by vendor lock-in disguised as innovation.
The solution? Protocol-agnostic AI architecture that builds for flexibility, not loyalty.
The Vendor Lock-In Trap
Modern AI is evolving faster than any single vendor or protocol can move. New models drop monthly, orchestration layers shift, and interfaces change. If your infrastructure is anchored to specific implementations, you’re building tomorrow’s technical debt today.
Too many platforms promise cutting-edge capabilities while quietly tethering you to their specific approach. When the next breakthrough arrives (and it will), you face costly migrations or accept system limitations.
Forward-thinking architects are solving this with protocol-agnostic infrastructure that integrates multiple standards simultaneously, particularly Model Control Protocol (MCP) and Google’s Agent-to-Agent (A2A) framework. Both of these protocols will evolve.
Two Protocols, One Flexible System
Many view MCP and A2A as competing standards, but they’re more so complementary tools that solve different problems:
MCP creates standardized connections between language models and external tools through a JSON-RPC interface. Think of it as a universal adapter letting AI agents securely access databases, APIs, and cloud services in real-time.
A2A enables multiple AI agents to coordinate activities. Agents can delegate specialized tasks, collaborate on complex problems, and orchestrate sophisticated workflows.
Together, they provide both the connectivity (MCP) and coordination (A2A) needed for enterprise-grade AI implementations.
Real-World Integration Patterns
Here’s how this plays out in practice:
Automated Data Pipeline Orchestration: An enterprise application needed to automate their customer segmentation pipeline.
- An orchestrating agent (via A2A) pulls historical transaction data using MCP-connected platforms.
- A forecasting agent runs predictive models to identify profitable segments.
- An activation agent automatically adjusts downstream system configurations.
Dynamic Resource Allocation: A performance optimization system transforms quarterly planning into daily optimization.
- A forecasting agent uses MCP to pull real-time performance data.
- An explainability agent translates model outputs into actionable budget recommendations
- Resource allocation happens automatically across multiple service endpoints
Real-Time Data Enrichment: An application needed to provide immediate personalized responses with minimal user data.
- Upon API request, an agent uses MCP to enrich profiles via internal history and third-party services.
- Enriched data powers real-time personalization across multiple touchpoints.
- The entire process completes within acceptable latency thresholds.
The Architecture Principles
Effective protocol-agnostic systems follow three core principles:
- Each protocol integration exists as an independent module. Teams can add new standards or swap individual agents without architectural changes.
- All connections operate through authenticated, encrypted channels with granular permission controls. Teams maintain full visibility into data access and agent actions.
- Computational resources scale independently of protocol overhead, handling hundreds or millions of requests without protocol-specific bottlenecks.
Building for an Uncertain Future
Thousands of MCP servers already exist for services and data sources. Major companies are rapidly adopting A2A, but new standards will inevitably emerge. Even today, sharing complete context through these protocols still needs more refinement.
Provided protocol-agnostic architecture is in place, should the above happen:
- Teams will integrate innovations without major pivots.
- Operations will maintain continuity without disruptive migrations.
- The focus shifts to business outcomes rather than specific technologies.
The Bottom Line
While MCP will most likely have legs, AI adjacent technology is moving at warp speed. Don’t build for today’s standards. Build for whatever’s next.
By staying protocol-agnostic, you avoid vendor lock-in, stop rewriting your stack every quarter, and shift focus from tools to outcomes. This isn’t just an infrastructure strategy—it’s how development teams stay fast, flexible, current, and competitive.
The platforms that once promised flexibility now feel like concrete. Protocol-agnostic AI architecture is the jackhammer that breaks you free.
Christian Monberg is the Chief Technology Officer and Head of Product at Zeta Global. Christian has served as a Zeta leader since 2017. He is responsible for overseeing the product and technical vision in support of Zeta’s business strategy, with his primary focus on developing and acquiring key AI, data and identity assets in support of the Zeta Marketing Platform. Prior to joining Zeta, Chris co-founded Boomtrain, an AI-powered marketing platform acquired by Zeta Global. Additionally, Chris was the VP of Interaction at Hornall Anderson and the founder of HAX, Omnicom’s first R&D lab, which enabled inter-disciplinary teams to redraw the boundaries of interactive design with cutting-edge technologies.
