Implementing an LLM-driven agent

Earlier in this section, we replaced deterministic decision logic with LLM reasoning. That shift only becomes meaningful once everything else is assembled into a working agent. This lesson exists to show how state, tools, workflows, and an LLM come together into a single, running loop that behaves coherently and safely over time.

Assembling state, tools, workflows, and LLM reasoning

An LLM-driven agent still relies on the same core components as a classical agent. It maintains explicit state, exposes tools as callable functions, and follows a workflow that gives structure to its behavior. The difference is that decision-making inside that structure is delegated to the model.

State is represented in ordinary Python data structures. Tools remain plain Python functions with clear inputs and outputs. The LLM is given a compact view of the current state and available actions, and asked to decide what should happen next.

Structuring the main AI agent loop

The main loop follows a familiar sense–decide–act–update shape. Sensing gathers input and reads state. Decision-making is handled by calling the LLM. Acting invokes tools chosen by the model. Updating applies the results back to state.

The loop itself stays simple and explicit. Control flow remains in Python, even though reasoning happens elsewhere.

while agent_is_running:
    context = build_llm_context(state, tools)
    decision = ask_model(context)
    result = run_tool(decision)
    update_state(state, result)

Executing the loop over multiple iterations

An agent rarely runs just once. Each iteration feeds the results of the previous step back into the next model call. Over time, the agent accumulates state and produces observable progress toward its goals.

Because the loop is explicit, it is easy to see where iterations begin and end. Each pass through the loop represents a single, traceable step in the agent’s behavior.

Producing observable behavior at each step

A working agent should make its actions visible. This might mean writing files, generating HTML pages, or printing progress information to the console. Observable behavior confirms that decisions are being translated into real effects.

html = render_planet_page(state["planet"])
with open("mars.html", "w") as file:
    file.write(html)

Each iteration should leave behind evidence of what the agent decided and did.

Maintaining correctness and safety during execution

Even with an LLM involved, correctness and safety remain the responsibility of the surrounding code. Tool names and arguments are validated before execution. State updates are checked before being applied. The loop enforces boundaries that the model cannot cross.

By keeping control flow, validation, and side effects deterministic, the agent remains predictable. The LLM contributes reasoning, not authority.

Conclusion

At this point, all the necessary pieces of an LLM-driven agent are in place. State, tools, workflows, and model reasoning are assembled into a single loop that runs repeatedly and produces visible results. The agent is no longer theoretical; it is a concrete program that can be inspected, tested, and extended with confidence.