I Thought AI Agents Were Magic. Turns Out They’re Just Structured LLMs.

December 15, 2025

When I first started learning about AI agents, I assumed they were something fundamentally different from normal AI.

Not just models. Not just prompts.

Agents felt like a separate category something that could do everything. Think, plan, act, decide, repeat. Almost like software with its own initiative.

That assumption didn’t survive contact with building.

The Mental Model I Started With

In my head, an agent looked like this:

You give it a goal
It figures out the steps
It uses tools
It keeps going until the task is done

I treated the agent as the intelligence.

So when things broke loops, wrong actions, confident mistakes, I assumed I hadn’t made the agent “smart enough” yet.

That was the wrong conclusion.

What an Agent Actually Is

At some point, it clicked.

An agent is not magic. It’s not a new kind of intelligence.

It’s an LLM with privileges.

The model itself doesn’t change. What changes is:

what it’s allowed to do,
what it can call,
and how its outputs are interpreted by the system.

Those permissions are what we call tools.

Once I saw this, the mystery disappeared.

Tools Are the Real Power

An LLM, on its own, can only produce text.

An agent can:

call APIs,
read or write files,
query databases,
trigger workflows,
interact with other systems.

Not because it’s smarter but because we let it act.

The “agent” is just the LLM sitting inside an orchestration layer that decides:

when the model should think,
when it should act,
and how its actions affect the system state.

Orchestration > Intelligence

This is where my understanding shifted.

The hard part of building agents isn’t prompting. It’s orchestration.

deciding which step comes next,
handling failure states,
preventing infinite loops,
knowing when the agent should stop.

Most agent bugs aren’t model bugs. They’re control-flow bugs.

Once I started treating agents as systems, not personalities, everything became easier to reason about.

Discovering MCP Changed How I Think About Agents

Later, I learned about MCP (Model Context Protocol).

That’s when agents stopped feeling like isolated entities and started feeling like participants in a larger system.

With MCP, agents can communicate with external services in a structured way not just via http/https tool calls, but through standardized interfaces.

This matters because real applications don’t live inside one model.

They live across:

databases,
APIs,
internal tools,
other services.

MCP makes that interaction explicit instead of implicit.

Agents Don’t “Do Everything”

This was the biggest realization.

Agents don’t do everything. They coordinate.

They don’t replace systems, they sit between them. They don’t remove complexity, they manage it.

The intelligence comes from:

sequencing,
constraints,
and clear boundaries.

Not from autonomy alone.

How I Think About Agents Now

Today, my mental model is simple:

An AI agent is an LLM wrapped in an orchestration layer, given limited authority to act through tools, and constrained by structure.

That framing makes agents less exciting and far more useful.

Final Thought

If agents feel magical, they’re probably under-designed.

Once you understand where the power actually comes from permissions, tools, and orchestration they stop being mysterious and start being buildable.

And that’s when they become interesting.