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:

  1. APIs must be agent-friendly — expose them in a way LLMs can interpret, not just humans.

  2. Tools must support orchestration — not just one-off integrations.

  3. 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.