[_*Recent research from Forrester’s David Mooter*_](https://www.forrester.com/report/put-integration-and-mcp-at-the-center-of-your-agentic-ai-strategy/RES196982)_*Recent research from Forrester’s David Mooter*_* makes a few things very clear: the enterprises that win with agentic AI won't be the ones with the most agents — they'll be the ones with the best integration infrastructure behind them.*
If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration.
Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability. New protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A) are fundamentally reshaping how that integration layer needs to work.
Here's why now is the time to be thinking about this.
An AI agent without reliable, governed access to your systems is just an expensive chatbot. The moment agents need to take real action — querying a database, triggering a workflow, calling a partner API — they need an integration layer that was designed for *them*, not retrofitted from one built for humans.
Mooter's research identifies a structural shift underway: companies are being pulled beyond traditional "integration plumbing" because agents need agent-to-tool interfaces that carry semantic context, enforce granular identity and permissions, and present curated action surfaces that reduce context overload and unintended invocation.
This is the problem MCP was designed to address.
By providing a standardized protocol for how AI agents discover and invoke tools, MCP gives enterprises a common language between their agents and their backend capabilities.
But MCP adoption has outpaced governance and versioning standards — which means enterprises need transformation layers, agent-specific registries, and token-optimized API surfaces to make it enterprise-ready.
However, speed without guardrails is a liability, not an advantage. As Forrester's research on agentic AI makes clear, most enterprises remain stuck between promise and payoff — not because agents don't work, but because they lack orchestration maturity, executable governance, and disciplined nonhuman identity management.
Extending proven API governance practices to MCP servers isn't optional; it's the foundation of the way forward. The same principles that have governed how you secure, version, and monitor REST APIs need to apply to the tools your agents call. If you've already invested in API management discipline, you have a head start — now it's about extending that governance model into the agentic layer.
“*The chief problem to solve in this market instead becomes how to give AI agents real-time context and tools to act on enterprise data. Organizations should lay that foundation by leaning into their existing API and integration practices.”*
A few of Forrester's findings to highlight here:
The research from Forrester is clear: vendors that abstract tool complexity into stable, semantically rich action catalogs will become structural components of enterprise agentic stacks. The enterprises that establish integration and MCP governance early will have a durable architectural advantage over those that bolt it on later.
The question isn't whether your organization will need an agentic integration strategy. It's whether you'll have the foundation in place before your competitors do.
*Forrester clients can read the full report: *[_*Put Integration And MCP At The Center Of Your Agentic AI Strategy*_](https://www.forrester.com)_*Put Integration And MCP At The Center Of Your Agentic AI Strategy*_
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