Agentic AI ≠ MCP Servers ≠ API Strategies — But They Work Best Together
In the last 6 months, I’ve watched the GenAI space go through what I like to call buzzword soup.
The hype cycle has moved quickly:
First, Agentic AI dominated the conversation.
Then MCPs (Model Context Protocols) became the new hot topic.
And somewhere along the way, people started asking if APIs were becoming obsolete.
It’s no surprise there’s confusion. Terms get thrown around interchangeably, and product teams are left wondering:
“Which should we use — and when?”
The reality?
These aren’t replacements for one another — they’re complementary layers in an AI-native stack.
If you’re building for the future, it’s not about choosing between them — it’s about understanding how they interact.
Breaking Down the Roles
Let’s get specific:
1️⃣ APIs — The Foundation
APIs (Application Programming Interfaces) expose a fixed set of capabilities. The client — whether it’s an application, a service, or a script — must know the endpoint and how to ask for what it needs.
They’re predictable, stable, and well-understood. But they require upfront integration work.
2️⃣ Agentic AI — The Brain
Agentic AI refers to LLMs (Large Language Models) acting autonomously to complete tasks on behalf of a user. They can work with or without a human-in-the-loop.
What makes them powerful is reasoning + planning + execution — the ability to figure out how to achieve a goal, not just respond to a single query.
To actually execute tasks, these agents call APIs or use MCPs.
3️⃣ MCPs — The Bridge
Model Context Protocols are a way of exposing capabilities so LLMs can:
Discover what tools are available
Understand how to use them
Chain them together into multi-step workflows
Maintain context across steps
Think of MCP as the toolbox index for an AI agent — allowing it to work across systems dynamically, without being hardcoded to specific API calls.
The Key Insight
In this model, the “user” of an MCP isn’t human — it’s the AI agent.
That’s the paradigm shift.
Instead of designing APIs only for developers, you’re now designing interfaces for machines that reason.
Example: Customer Service AI Agent
Let’s say you’re building a customer support agent powered by LLMs.
With MCPs, your agent could:
Create a customer profile (onboarding)
Update a subscription (billing)
Trigger a refund or dispute (resolution)
—all through natural language + real-time tool discovery.
Before MCP, you’d need brittle, manually integrated API calls for each of these steps.
Now, you can expose capabilities in a way that lets the agent discover and use them autonomously.
What This Means for Builders
Shifting to an AI-to-system architecture changes your approach to system design:
APIs must be agent-friendly — expose them in a way LLMs can interpret, not just humans.
Tools must support orchestration — not just one-off integrations.
Systems must maintain context — enabling multi-turn, multi-step interactions.
The Future: AI-to-System, Not Just Human-to-System
We’re moving from a world where humans explicitly direct systems…
…to a world where AI agents navigate systems on our behalf.
The winning teams will be the ones who stop treating Agentic AI, MCPs, and APIs as separate silos — and start designing for how they work together.
👇 I’m sharing a visual (courtesy of Department of Product) that helped me map out these distinctions and overlaps — and I’d love to hear how your team is adapting to this shift.