Alan Shimel explores, through an interview with Kumar Chivukula, why traditional APIs fall short for AI-era needs and how the Model Context Protocol may redefine enterprise API strategies.

Why APIs Alone Won’t Cut It in the AI Era

Author: Alan Shimel

Kumar Chivukula, co-founder and CEO of CodeGlide.ai, discusses why conventional APIs struggle to keep up with AI-driven applications and how the Model Context Protocol (MCP) could offer a way forward for enterprises.

The Challenge with Traditional APIs

APIs have long been vital for data access and system connectivity, but they weren’t designed for contexts where AI models demand memory, context-awareness, and intent understanding. As a result, developers often write fragile glue code just to bridge gaps between shifting AI model requirements and legacy APIs.

Enter the Model Context Protocol (MCP)

Anthropic introduced MCP to provide a standardized method for making APIs context-aware and adaptable to AI needs. Unlike basic wrappers, MCP aims for deeper alignment with AI models, facilitating richer interactions and automated adaptation to model changes. However, implementing MCP isn’t simply a technical fix; it impacts the entire API lifecycle.

Complexity in the Enterprise

Enterprises manage tens of thousands of APIs, many of which are outdated, undocumented, or used only internally. With AI model requirements evolving rapidly, organizations risk operational bottlenecks if they treat MCP as just a quick patch. To avoid this, Chivukula emphasizes that a lifecycle-centric, automated approach is needed, including continuous security scanning and updates.

CodeGlide’s Approach

Platforms like CodeGlide position themselves to automate MCP server creation, upgrade management, and security at scale. By integrating MCP workflows with environments like GitHub—where millions of developers manage API code—enterprises can streamline API modernization and AI integration.

The Road Ahead

With the number of APIs worldwide exceeding a billion and the AI economy continuing to expand, the adoption of MCP or similar context protocols is likely inevitable. Chivukula and Shimel stress that enterprises must carefully plan their API modernization strategies to prevent complexity from becoming unmanageable. Continuous refactoring, robust change management, and security enforcement are key.

Key Takeaways

  • Traditional APIs weren’t designed for AI: lacking memory and context.
  • Model Context Protocol (MCP) enables context-aware, AI-ready APIs.
  • Successful MCP adoption requires a lifecycle approach and automation.
  • API platforms like CodeGlide can help by automating MCP server creation, updates, and security.
  • Integration with GitHub and other DevOps workflows is critical for scale.
  • Organizations must proactively manage API modernization to keep up with AI’s expanding role.

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