Model Context Protocol (MCP): structuring memory and action in agentic AI systems

As AI agents evolve—becoming more autonomous, contextual, and collaborative—the complexity of managing memory, reasoning, and task execution grows dramatically. What once followed a simple prompt–response flow now requires architectures that support multi-agent, multi-task environments with persistent context and scalable orchestration.

In this new landscape, a key challenge emerges: how to ensure that agents not only act, but act coherently—with continuity, memory, and alignment across interactions.

That’s where MCP—Model Context Protocol—comes into play.

What is MCP?

Model Context Protocol is an emerging approach for managing contextual memory and coordination among AI agents, especially in scenarios involving multiple LLMs (large language models), tasks, and timeframes.

Rather than treating each model interaction as stateless, MCP establishes a layer of abstraction and structure to:

• Track the evolution of knowledge and decisions over time;

• Manage access to contextual memory (shared or private);

• Define how and when agents exchange information or trigger actions.

Think of it as a “cognitive middleware” between models and tasks, allowing AI agents to operate not as isolated function calls, but as entities in a shared, persistent ecosystem.

Why does MCP matter?

Without a protocol like MCP, agent systems tend to fall into one of two traps:

1. Prompt spaghetti: long, hard-coded prompts trying to maintain memory, logic, and objectives all in one shot—fragile, hard to debug, and almost impossible to scale.

2. Agent silos: multiple agents acting independently, unaware of one another’s reasoning, decisions, or context—leading to contradictions, inefficiencies, and repeated work.

MCP solves this by establishing rules and contracts for how context is created, accessed, updated, and shared. It enables:

• Persistent memory over long interactions;

• Controlled visibility between agents;

• Standardization of multi-step reasoning and task execution.

How it works (at a high level)

An MCP-based architecture typically includes the following layers:

1. Agent layer

   LLM-powered agents, each with defined roles (e.g., Planner, Executor, Critic). These agents operate within defined scopes of memory and permission.

2. Context manager

   A layer responsible for storing, retrieving, and versioning context elements—like knowledge bases, state trees, action logs, and goals.

3. Protocol rules

   Definitions for how agents read/write context, when transitions occur, and how conflicts are resolved. This may include structured metadata, tagging, time windows, and memory segregation (e.g., public vs private).

4. Execution environment

   Where agents actually act—making API calls, querying systems, generating documents, or interacting with users.

MCP vs. Orchestration frameworks

You might wonder: how is MCP different from orchestration tools like LangChain, Semantic Kernel, or CrewAI?

Those tools offer agent frameworks—ways to build and connect LLM-based agents with plugins, chains, and tools.

MCP, on the other hand, focuses on the underlying protocol layer that governs contextual integrity and memory sharing between agents. It can complement orchestration tools, but with stricter discipline on how agents reason and remember.

In that sense, MCP is less about infrastructure, and more about epistemic architecture: How do we ensure that agents share truth, maintain coherence, and act in alignment?

When does MCP become necessary?

MCP isn’t required for all AI use cases. It becomes critical when systems have:

• Multiple agents acting in parallel or over time;

• Long-lived goals, requiring persistent memory and adaptation;

• Complex tasks, with dependencies, constraints, and feedback loops;

• Need for trust and auditability, where agents’ decisions must be explainable and traceable.

Some concrete examples include:

• AI-powered help desks that learn and adapt across thousands of customer interactions;

• Compliance agents that monitor decisions made across systems;

• Multi-agent planning systems for logistics or R&D, where agents collaborate over weeks or months.

VX’s view on MCP

In VX Technology’s applied work with agentic architectures, we’ve found that without a structured protocol layer, most systems eventually become brittle, hard to maintain, or unreliable in production.

MCP offers a scalable path forward: allowing teams to start with narrow-scope agents, but evolve into modular, memory-aware ecosystems where context is a first-class citizen.

In our internal frameworks, we use MCP principles to manage state trees, role hierarchies, and agent reasoning boundaries—reducing “hallucinations,” improving observability, and aligning outcomes with business objectives.

Final thoughts

As AI agents take on more responsibility—across customer support, internal operations, decision-making, and beyond—their success will depend not just on intelligence, but on structure.

Model Context Protocol isn’t a silver bullet. But it’s a fundamental shift toward building AI systems that remember, coordinate, and evolve—rather than endlessly starting from scratch.

In this new era of multi-agent AI, context isn’t just helpful.

It’s protocol.

References

• McKinsey & Company (2023). “The Economic Potential of Generative AI”

• BCG (2023). “Why Multi-Agent Architectures Will Shape the Next Generation of AI Products”

• ThoughtWorks (2024). “Beyond Orchestration: Context Management for Agentic Systems”

• OpenAI Developer Forum. “Memory Management Patterns for Multi-Agent Systems”

• VX Technology. Internal AI Agent Framework Documentation (2024)