Agentic AI is moving fast. Enterprises are shifting from experimentation to real deployments – they’re using AI agents to act, not just answer. This should put integration at the forefront. After all, an AI agent without integration is merely an answer engine. It cannot take action. Yet many organizations are repeating a familiar mistake: building AI capabilities in isolation and disconnected from the teams that already govern integration.

Teams worry about architectural uncertainty, evolving standards, and unclear security models. At the same time, fragmented experimentation is creating silos and technical debt.

These issues are not new. They mirror the early days of APIs.

The difference? The pace of AI adoption is much faster, and the consequences of weak integration governance are more immediate. I explore this in my recent Forrester report, Govern MCP By Extending API Governance To AI Agents.

The Key Agentic Link: The API Integration Team

Many organizations already have a proven integration governance capability: their API integration team. This team understands how to manage distributed systems at scale. It has experience with:

  • Security and access control.
  • Versioning and lifecycle management (you do realize that MCP servers and agent cards need versioning and lifecycle management, right??).
  • Observability and operational resilience over distributed systems.
  • Catalogs to publish reusable integration assets and onboard clients, just as you should be doing with LLMs, MCP servers, and agent cards.

Yet in many enterprises, AI initiatives are being led separately without sufficient alignment to this team.

That is a mistake.

To view agentic AI systems as just models and data lakes is naive. They are applications composed of services, APIs, and workflows. Forrester data shows that organizations with high readiness to adopt agentic AI are prioritizing integration and change management over the next 12 months, while those with low readiness are prioritizing model capabilities.

This is a change for agentic AI. Traditionally, with AI/ML, the fundamental problem to solve is shoveling all your data into a data lake where it can train the model. But with agentic AI, you buy the models. Now the fundamental problem is connecting those models with real-time context. This emphasizes data in motion and connectivity, not data at rest. The focus thus changes from data lakes to integration and, in particular, to API management.

Treat Agentic AI As Part Of Your Integration Strategy

You must bring AI into your existing integration strategy from day one. Treat agent interactions as managed integrations, subject to the same expectations for reliability, security, and lifecycle management. From a governance perspective, this means extending proven API practices rather than starting from scratch. Security policies, versioning approaches, observability patterns, and catalogs for discovery and onboarding already exist. API management vendors are rapidly expanding these capabilities from REST to LLM and MCP proxies. Applying them to AI agents creates consistency across digital channels and reduces the likelihood of surprises as adoption scales.

Want to discuss how to align your API integration team to agentic AI? Forrester clients may request a call with me to discuss further.

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