what actually makes a system agentic?

LLM + Tools ≠ Agent

I used to think an AI agent was simply:

LLM + tools = Agent

After exploring agentic system design, I’m starting to see it differently.

The LLM is only one component.

The real engineering challenge is designing the execution loop around it.

A basic LLM application looks like:

"Input → LLM → Response"

An agentic system looks more like:

"Goal → Reason → Act → Observe → Reason → Act → ... → Done"

For example:

1. Receive a user request
2. Understand the goal
3. Determine what information is required
4. Select the appropriate tool
5. Execute the tool
6. Inspect the result
7. Decide the next action
8. Retry, change strategy, or escalate if required
9. Stop when the goal is achieved

This introduces problems that are very familiar to software engineers:

• State management
• Tool selection
• Error handling
• Retries and timeouts
• Guardrails
• Termination conditions
• Human-in-the-loop
• Observability and tracing

So I'm beginning to think that reliable agent development is not primarily a prompt-engineering problem.

It is a system-design problem with an LLM inside the system.

The question I'm exploring now is:

«How do we design the execution loop so an agent can reason and act without becoming unpredictable?»

For developers building agents in production, what has been the hardest part for you: reasoning, tool orchestration, memory/state, or controlling the execution loop?