The Power of MCP: How Real-Time Context Unlocks Agentic AI for the Modern Enterprise 

The Evolution of AI: From LLMs to Autonomous Agents

In just a few short years, AI as we know it has dramatically evolved. While ChatGPT asserted itself as the LLM everyone knew and loved, other prominent AI interfaces joined the scene. Anthropic’s Claude, Google’s Bard (which later became Gemini), and another tool named Perplexity became our helpful desktop companions.

From the outset, conversational LLMs were both fun to use, and helpful for everyday tasks. But they weren’t considered sufficient for everyday work —not until late 2023 when their ability to handle complex tasks significantly improved. 

Soon enough LLMs could generate not just text-based outputs, but images, videos, and even audio files. This led to an explosion of AI tools to assist writing, coding, and notetaking. Over time, AI evolved from simple task-takers to “agentic systems,” capable not only of answering instructions but acting autonomously, even using other tools themselves, to perform multi-step operations. 

Fast forward to today, and many enterprises are still exploring how they can best leverage AI. Tools like conversational LLMs have proved extremely useful for ad-hoc tasks. Yet these tools are only so effective in isolation—siloed off from the data and contexts of the wider organization. 

The next step: to embed AI tools in the enterprise by connecting them with the data, systems, and contexts they need to make an impact. 

The Challenge of Connecting Agents to Systems and Tools

As agentic AI emerged, it became clear that context was critical to better outcomes. Yet connecting agents to relevant sources was difficult and time consuming, as developers struggled with a patchwork of custom-built integrations and hardcoded APIs. 

For enterprises, building these interfaces between agents and databases has been slow and complex. Up to now, this has hindered their ability to test and iterate agentic systems across the business. Enterprises need a faster, more scalable way to connect sources and agents, without labor-intensive custom-coding for each application and database.

Enter Model Context Protocol (MCP), a new, standardized protocol enabling AI models to interface cleanly with external tools and data in a structured format.

Like the “USB-C” of AI, MCP offers a universal standard that makes it much faster and easier to connect agentic AI with tools and databases. Before MCP, bringing valuable context to agents at scale was insurmountable for enterprise companies. MCP promises to make this process fast and straightforward, finally enabling engineers to embed AI in the enterprise.

With MCP, developers can plug agents into a variety of tools and data sources, without having to individually code integrations or implement API calls. This is a gamechanger: not just for faster time-to-value when it comes to leveraging context-rich AI, but for building robust, agentic systems at scale. 

In one test by Twilio, MCP sped up agentic performance by 20%, and increased task success rate from 92.3% to 100%. Another study found that MCP also reduced compute costs by up to 30%. The results are clear. MCP isn’t just an accelerator, but the new standard for enterprise AI. 

A New Standard for Agentic Systems

Invented by Anthropic, MCP is an open standard for managing and transferring context between AI models, tools, applications, and agents. It enables AI systems to remember, share, and reuse information across tools and environments by exchanging structured context in a consistent way.

MCP lets agentic systems learn and use context in powerful ways. The context, however, is still critically important. The better your data—its speed, quality, governance, and enrichment—the better context you can send to intelligent systems through MCP. 

Striim’s Value: Delivering Real-Time Data Context  

From simple interfaces, to tools, agents, and now embedded in enterprise infrastructure—generative AI has come a long way in just a few years. Today, MCP represents a huge opportunity for enterprises, but it calls for a new mandate: the need for a real-time, well-governed, AI-ready data access for agents without compromising production workloads, data sensitivity, or compliance. 

Directly exposing production operational data stores to agents is a recipe for performance and governance headaches. High-frequency queries from AI workloads can create unpredictable spikes in load, impacting mission-critical transactions and degrading end-user experiences. It also increases the risk of compliance violations and accidental data exposure. 

The safer and smarter approach is to continuously replicate operational data into secure zones that are purpose built to serve agents via MCP servers. These zones preserve production performance, enforce access policies, and ensure AI systems are working with fresh, well-governed data while allowing controlled write-back when needed, without ever touching the live systems that run the business.

That’s where integrative solutions like Striim come in. Sitting at the heart of this new architecture, Striim’s MCP AgentLink offers a continuous, real-time, cleansed, and protected operational replica in safe, compliant zones—giving agents fresh, accurate data without exposing production systems. With a growing number of operational databases such as Oracle, Azure PostgreSQL, Databricks, and Snowflake announcing support for MCP, Striim ensures these systems can feed governed, AI-ready context directly into MCP servers in real time.

Specifically Striim:

• Replicates operational databases (e.g., Oracle, SQL Server, PostgreSQL, Salesforce) in real-time to read-only, agent-safe destinations, PostgreSQL clusters.
• Processes and transforms streaming data to remove PII, enriches it with context, and prepares it for agentic consumption.
• Routes agent-generated writes to a safe staging layer, validates them, and syncs them back to source systems through its stream processing engine.
• Powers event processing to deliver decision-ready, well-structured event data where it’s needed most.

Simply put, Striim is the real-time, intelligent, and compliant middleware that bridges enterprise systems and MCP agent workloads. With Striim MCP AgentLink, enterprises can finally realize the promise of AI by connecting it with their existing tools and databases.