What an AI agent loop is (vs normal chat)

AI agent vs plain LLM chat

You already know plain chat with an LLM: you send some text, it predicts the next tokens and sends them back. An AI agent adds a loop of planning–acting–observing around that same prediction ability.

In plain chat:

  • You: "What's the weather in Paris tomorrow?"
  • Model: Generates an answer from its training data and current prompt.
  • It cannot actually look up today’s forecast unless you manually paste it in.

In an agent loop, the model is allowed to:

  1. Decide what it needs (e.g. "I should call a weather API").
  2. Call a tool.
  3. Look at the tool’s result.
  4. Decide what to do next (call another tool, ask a question, or answer you).

So the core difference:

  • Plain chat = one-shot or multi-turn message completion.
  • Agent = repeated plan → act → observe → update using tools and memory.

A quick comparison:

AspectPlain LLM chatAI agent loop
Main behaviorCompletes textDecides actions over time
External toolsNone (unless you paste output)Built-in API / tool calls
MemoryMostly current promptExplicit state & history
StructureTurn by turn with userInternal loop of steps per task

Concrete example

Task: "Book me a train from Berlin to Munich tomorrow morning."

  • Plain chat model might:

    • Answer: "You can check trains on the Deutsche Bahn website..."
    • It just describes what you should do.

  • Agent loop could:

    1. Plan: "I should search trains via the TrainAPI tool."
    2. Act: Call search_trains(origin=Berlin, dest=Munich, date=2026-06-23, time_range=morning).
    3. Observe: Get real options back.
    4. Plan: "Now pick the best option and call book_travel."
    5. Act: Call book_travel(option_id=abc123).
    6. Observe: Booking confirmation.
    7. Answer you: "I’ve booked train ICE 123 at 08…" (plus details).

Here the LLM is still "just predicting text", but the protocol around it makes those tokens either:

  • A human-facing answer, or
  • A machine-readable tool call that the system executes.
1 / 6