Enterprise AI is advancing quickly, but not always coherently. Many teams have built powerful point solutions with large language models, only to discover that the bigger challenge isn’t intelligence, but continuity. The problem isn’t what AI can understand in a single interaction, but what it forgets between them. In complex workflows with multiple tools, data sources and agents, context loss becomes the limiting factor. Model Context Protocols (MCPs) are beginning to emerge as the architectural backbone for AI systems that can actually scale, share memory and stay aligned across tasks.
A project I consulted on for a patient recovery app showed this weakness perfectly. A patient asked why their leg hurt more than yesterday. The system checked his medical record, which noted that pain was expected and his vitals, which were fine. The app’s useless answer was simply that some pain is normal. An effective context protocol would have connected the complaint to the wearable data showing his activity had spiked the day before. The system failed because it didn’t have a shared memory to link these two simple facts. It had data points, but no capacity to connect them over time.
At their core, MCPs define how different AI components, such as language models, databases, interfaces and agents, communicate and manage context. They create a consistent structure for remembering what is already known, what has been asked, what has been answered and what still needs to be done. Without a shared protocol for context, even the best-performing models are effectively amnesiacs, unable to build toward outcomes over time.
Imagine linking a weather agent, a calendar agent and a smart home agent to automate your coffee maker on rainy work-from-home days. The workflow would likely fail on its first run. You would find the weather API uses the PDT timezone, your calendar uses PST and the smart plug defaults to UTC. Each agent is correct in its silo, but the system is useless without a shared memory to normalize something as fundamental as time.
Cohesiveness is especially important as organizations shift from simple chatbots to distributed agent-based architectures. Each of these agents might have its job: Search the intranet, write a summary, send a follow-up email, update a database. But without a standardized way to pass along state and history, these agents duplicate work, misinterpret intent, or make conflicting decisions. The net result: More orchestration code, more monitoring overhead and less trust in automation. MCPs solve this by giving every system a shared memory structure to refer to and contribute to.
From Fractured Context to Shared Memory
Most organizations already have pieces of what an MCP offers, scattered across logs, audit trails and vector databases. However, the lack of consistency between them means that context is fragmented and fragile. An effective MCP stack provides four foundational layers. First is the context vault, where session-level data, inputs, outputs, metadata, goals and preferences can be stored and queried over time. This must support secure access, retention policies and detailed provenance since it often holds sensitive internal information.
The second layer is the connector interface. Enterprises rely on a wide array of tools: Cloud drives, ticketing systems, analytics dashboards, CRM platforms and internal search engines. Each of these needs to be able to read from and write to the MCP context format, mapping native data into a shared structure. The third component is a policy engine, which determines what data should be preserved, shared, or redacted, critical for ensuring privacy, regulatory compliance and reducing noise. Lastly, a router and caching layer help minimize latency for high-frequency queries and keep systems responsive when multiple agents are accessing the same context in parallel.
Take a connector for a personal calendar application. For obvious security and trust reasons, the system can’t store a user’s password, and a single master credential for the service is out of the question. The only sound architectural pattern here is to use OAuth 2.0. This design passes the user to their calendar provider’s own consent screen to explicitly approve access. From that point on, the connector’s sole responsibility is to securely manage the specific, revocable token the user granted. It ensures every action taken is auditable and tied directly to that initial user consent.
Why Standardization Beats Scale Alone
Even though the tooling and architecture are evolving fast, standardization is the real unlock. A vendor-neutral context protocol lets teams avoid getting locked into proprietary ecosystems. It allows for experimentation with different models and services without rewriting core logic. It also simplifies compliance, debugging and auditing, since logs and workflows are interoperable and transparent. This isn’t about building a one-size-fits-all schema; it’s about defining a lightweight but flexible context envelope that can evolve with the stack.
We’ve seen similar stories play out in previous generations of technology. The web needed HTTP. Databases needed SQL. APIs needed REST. AI systems that span multiple tools, models and memory systems now need their own underlying context protocol. MCPs offer that foundation. Without it, teams end up maintaining brittle middleware and over-engineering every integration.
For organizations ready to get started, the first step is to audit their current systems for context gaps. Where are users repeating themselves? Where do agents lose track of goals? Where are tools duplicating the same query across multiple systems? These are almost always signs of broken or absent memory layers. From there, it’s worth starting small: Define a minimal MCP schema with a few consistent fields, including task ID, user ID, timestamp and source. Gradually bring key services into that structure, particularly the ones that power high-value decisions or user-facing outputs.
As the architecture grows more mature, governance policies must keep pace. Retention periods, masking rules and access control lists need to be built in early, not as a retrofit. And impact should be measured in operational terms. A well-implemented MCP should reduce time-to-deploy for new agents, cut down on repetitive engineering work and make workflows easier to debug and scale.
The Road Ahead for Context-Driven AI
Looking ahead, the rise of agentic AI and multi-modal applications will only increase the pressure on teams to maintain shared memory and coordination. Model Context Protocols offer a way to turn loosely coupled tools into intelligent, stateful systems that evolve over time. While individual vendors will continue to differentiate on performance, connectors, or privacy features, the protocol layer should ideally remain open and interchangeable.
The organizations that embrace MCPs early will not only move faster but also with more reliability and fewer coordination failures. As AI moves from hype to infrastructure, context-structured, portable and enforceable may be the most critical layer of all.
My conviction is that MCPs are the foundation for the future agentic world. For that future to be robust and innovative, it cannot be built on proprietary, closed ecosystems. To that end, I hope to influence this direction through action, not just words.
