Large language models are powerful, but calling them “agents” on their own is a category mistake. This confusion shows up constantly in real projects, especially when people expect a single prompt to behave like a system that can reason, act, and adapt.
If you’ve built anything beyond a demo, you’ve likely hit this wall already.
This article explains why LLMs alone are not agents, what’s missing, and where the responsibility actually lies when building agentic systems.
What an LLM Actually Does
At its core, an LLM performs one job:
Given a sequence of tokens, predict the next token.
Everything else—reasoning, planning, explanation—is an emergent behavior of that process.
Important constraints:
- The model has no memory beyond the prompt
- It has no awareness of outcomes
- It cannot observe the world unless you feed it observations
- It cannot act unless you explicitly wire actions
An LLM doesn’t “decide” to do something. It produces text that describes a decision when asked.
That distinction matters.
Why This Fails in Real Systems
When people treat an LLM as an agent, they usually expect it to:
- Decide what to do next
- Verify its own outputs
- Recover from mistakes
- Adapt to new information
But none of those happen automatically.
An LLM will happily generate:
- A plan it never executes
- A correction without knowing it failed
- A confident answer with missing data
Because it has no feedback loop.
The Missing Ingredient: Control Flow
Agency comes from control flow, not from language generation.
An agent needs:
- A goal
- A loop
- Actions
- State
- Feedback
An LLM provides none of these by default.
When you prompt a model to “think step by step,” you’re not giving it agency—you’re just asking it to simulate reasoning in text.
Once the output is produced, the model is done.
Planning Is Not Acting
A common trap is equating planning with agency.
You ask the model:
“Plan how to solve this problem.”
It produces a clean, multi-step plan.
But nothing happens.
The model:
- Doesn’t execute the steps
- Doesn’t check if a step succeeded
- Doesn’t revise the plan based on results
Without execution and observation, a plan is just text.
Real agents operate in a loop where each step changes the world—or at least the system state—and the next decision depends on that change.
Tool Calling Doesn’t Automatically Create an Agent
Even with tool calling or function calling, an LLM is still not an agent on its own.
Why?
Because the model does not:
- Decide when to stop
- Enforce constraints
- Validate tool outputs
- Retry intelligently
Those behaviors must be implemented around the model.
The LLM can suggest actions.
Your system must decide whether they’re allowed and what happens next.
Where Developers Usually Misplace Responsibility
The most common architectural mistake is expecting the model to manage:
- State
- Errors
- Retries
- Costs
- Safety
LLMs are not state machines.
They are not schedulers.
They are not supervisors.
When systems fail, it’s usually because:
- There’s no max-step limit
- There’s no failure mode defined
- The agent keeps “thinking” without progress
- No one can explain why a decision was made
That’s not an AI problem. It’s a system design problem.
What Actually Turns an LLM Into an Agent
An LLM becomes part of an agent only when embedded inside a loop.
That loop must:
- Provide observations
- Accept decisions
- Execute actions
- Update state
- Decide when to stop
The agent is the loop.
The LLM is just one component inside it.
Once you see this clearly, the hype disappears and the engineering work becomes obvious.
A Useful Mental Shift
Instead of asking:
“Can the model do this?”
Ask:
“What decisions am I allowing the model to influence?”
This reframing forces you to think about:
- Boundaries
- Permissions
- Failure modes
- Debuggability
And it keeps systems stable.
A Short Closing Thought
LLMs are powerful reasoning engines, but agency does not come from intelligence alone. It comes from structure, feedback, and limits.
Treat models as components, not actors.
The moment you do, agentic systems stop feeling magical—and start feeling buildable.
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