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From Protocols to Control Planes: What MCP Teaches Us About Building Agentic Systems at Scale

By Alexander Chernov. First published on LinkedIn, 2026-03-29. Read the original.

I was recently selected to speak at Optimized AI Conference 2026 and the MCP Dev Summit North America 2026.

Due to logistical constraints, I was unable to attend. However, the material prepared for these sessions reflects a direction I believe is becoming structurally important for modern AI systems.

This article summarizes that perspective.

The Initial Assumption: MCP as a Lightweight Protocol

Model Context Protocol (MCP) is often introduced as a simple abstraction:

a stateless interface for tool invocation between agents and external capabilities.

At small scale, this mental model holds.

Agents call tools. Tools respond. The system behaves predictably.

What Actually Happens in Production

Under real-world conditions, this abstraction begins to break.

Once deployed in environments with:

  • multiple concurrent agents
  • dynamic capability evolution
  • Kubernetes-based execution
  • continuous deployment and scaling

MCP stops behaving like a protocol.

It starts behaving like a distributed system.

And more specifically:

a control plane.

The Structural Shift

This transition is not accidental—it is structural.

The moment you introduce:

  • session continuity across unstable infrastructure
  • replay and idempotency requirements
  • shared capability catalogs
  • multi-tenant policy constraints

you are no longer designing a protocol.

You are designing a system that must coordinate state, enforce rules, and maintain consistency across independent actors.

In other words:

you are building a control plane.

The Failure Modes (Observed and Reproducible)

When this shift is not acknowledged, systems tend to fail in predictable ways:

1. Session Instability Under Infrastructure Churn

Pods restart. Routes change. Agents lose context.

Without a control-plane layer, session continuity becomes fragile.

2. Replay and Idempotency Gaps

Agent interactions are not inherently safe to replay.

This leads to duplicated actions, inconsistent outcomes, and non-deterministic workflows.

3. Capability Catalog Drift

Tools evolve. Interfaces change.

Without strong control-plane governance, different agents operate against inconsistent views of the system.

4. Backpressure and Overload

As concurrency increases, systems lack mechanisms to regulate flow.

The result is cascading failure rather than graceful degradation.

5. Multi-Tenant Policy Ambiguity

In shared environments, enforcing who can do what—and under which conditions—becomes a first-class concern.

This cannot be reliably handled at the protocol level.

A More Accurate Mental Model

A more robust way to think about MCP in production is:

  • MCP provides minimal primitives

  • A control plane layer manages:

    • routing
    • policy enforcement
    • consistency
    • observability
    • lifecycle management

This mirrors patterns seen in:

  • service meshes
  • event-driven systems
  • actor models
  • distributed data platforms

Toward Agentic Control Planes

This perspective has been shaping my ongoing work around:

  • agentic datasets (datasets as active participants in execution)
  • descriptor-driven systems (declarative control surfaces)
  • policy-aware execution models
  • large-scale multi-agent environments (10⁶+ agents)

The key idea is simple:

decisions, policies, and capabilities should be treated as first-class control-plane artifacts—not implicit side effects of agent execution.

Why This Matters

As AI systems move from isolated workflows to persistent, multi-agent environments, the cost of ignoring control-plane design increases dramatically.

Without it:

  • reproducibility degrades
  • governance becomes reactive
  • system behavior becomes difficult to reason about

With it:

  • systems become observable
  • policies become enforceable
  • behavior becomes auditable and controllable

Closing

Although I wasn’t able to present this work in person, I’m continuing to develop these ideas in both research and production contexts.

I believe this transition—from protocol thinking to control-plane thinking—will define the next generation of agentic infrastructure.

I’m looking forward to contributing these directions in future venues.

© 2026 Alexander Chernov. All rights reserved. First published on LinkedIn, which remains the canonical version; this page is a reprint by the author.