Come to grips with MCPs to succeed with agentic AI

CTOs must come to grips with another new kid on the AI block—Model Context Protocols (MCP). These frameworks enable AI models, particularly agentic AI systems, to operate more effectively within enterprise environments. They define how context data is structured and maintained, allowing AI systems to understand and adapt to wide-ranging operational scenarios.

At their core, MCPs provide a formal description of the contextual information present in a context-aware system. Such a system dynamically adapts its behavior based on environmental data, user input, or real-time interactions. This involves detailing the surrounding elements for the system and offering a mathematical interface and behavioral description of the environment. By doing so, MCPs enable AI systems to interpret and respond to contextual cues, enhancing their decision-making.

For example, A customer support chatbot integrated with an MCP can retrieve relevant customer information from CRM systems (such as Salesforce), past support tickets, and real-time website interactions to provide personalized assistance. Without context awareness, the AI would likely ask repetitive questions or provide irrelevant responses. With MCP, the AI automatically adapts, reducing friction and improving efficiency.

MCPs support better decisions, improved experience, and operational efficiencies

For enterprise CTOs, integrating MCPs into AI strategies offers several advantages:

• Enhanced decision-making: By leveraging contextual information, AI systems can make more informed decisions, leading to improved operational efficiency.
• Improved user experiences: Context-aware AI can tailor interactions based on user behavior and preferences, resulting in more personalized and engaging experiences.
• Operational efficiency: AI systems equipped with MCPs can autonomously adapt to changing environments, reducing the need for manual interventions and streamlining processes.

Agents get a better understanding of the environment they operate in

Agentic AI refers to autonomous systems capable of making decisions and performing tasks without human intervention. The integration of MCPs into these systems allows them to better understand their operating environment, leading to more accurate and reliable outcomes—as outlined in the example of customer support automation described above.

Beyond agentic AI, MCPs enhance traditional AI applications by enabling them to process and analyze unstructured data, perform contextual analysis, and interact more naturally with users. This shift from static, rule-based systems to dynamic, context-aware models represents a significant evolution in AI capabilities.

Anthropic offers pre-built MCP servers for multiple enterprise systems

Anthropic has introduced an open-source standard MCP designed to connect AI assistants to various data sources, including content repositories, business tools, and development environments. This protocol aims to enhance the relevance and quality of responses generated by AI models by providing a universal method for data integration.

Anthropic has developed pre-built MCP servers for popular enterprise systems such as Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer.

You will find examples of MCP in action at Replit (an online coding platform utilizing MCP to enhance its AI-driven coding assistance features); Codeium (an AI code assistant that leverages MCP for more efficient code generation and integration); and Sourcegraph (a code search and navigation tool employing MCP to provide advanced code intelligence and search functionalities). HFS Hot Tech Lyzr has also announced it is applying MCP to support agentic AI.

The Bottom Line: Harness MCPs to ramp up the adaptability and responsiveness of your firm’s AI applications

For enterprise CTOs aiming to harness the full potential of AI, embracing Model Context Protocols is likely to become crucial. These protocols not only enhance the functionality of agentic AI systems but also improve the overall adaptability and responsiveness of AI applications across the organization. By integrating MCPs, enterprises can achieve more personalized user interactions, streamlined operations, and a competitive edge in the AI-driven market landscape.