How Agent Loops Work

Agent loops are the control mechanism behind most modern agent systems. They decide whether the model should answer now, call a tool, or continue reasoning with more context.

The loop is simple in concept:

1. read the current state
2. choose an action
3. execute the action
4. update state
5. repeat or stop

The complexity comes from deciding when to stop and how to keep the loop from wasting time or money.

Why loops exist at all

A single prompt is often not enough for real work. Consider a request like:

Compare three cloud providers, estimate cost, and recommend one for a startup workload.

That task may require:

• gathering current facts
• doing arithmetic
• comparing tradeoffs
• refining the answer after new information appears

A loop allows the model to work in stages instead of pretending it already has everything it needs.

The observe-think-act pattern

Most loops can be described with three verbs:

Observe

Read the current user goal, prior tool outputs, and current memory.

Think

Choose what should happen next. This may be internal reasoning, planning, or selecting a tool.

Act

Execute the step. That can mean returning a final answer or calling a tool.

Here is a minimal version:

for step in range(max_steps):
    action = model.plan(goal=goal, state=state)

    if action["type"] == "finish":
        return action["answer"]

    if action["type"] == "tool":
        tool_result = tools[action["name"]](**action["args"])
        state.append(tool_result)

raise RuntimeError("Agent exceeded max steps")

The max_steps guard matters more than many teams expect.

Stopping conditions

An agent loop needs a clear exit strategy. Typical stopping conditions include:

• a final answer is produced
• a confidence threshold is reached
• a required fact has been retrieved
• a step limit is reached
• a cost or latency budget is exceeded

Without stopping conditions, loops can become expensive very quickly.

Why loops fail

Loop failures are rarely random. They usually happen because:

• the model keeps calling the wrong tool
• the state becomes noisy or bloated
• the loop has no strong success criteria
• the model cannot tell whether it has enough information
• the system allows too many cheap-looking but cumulatively expensive retries

In practice, a weak loop feels like "the agent keeps doing things, but nothing useful is happening."

Loop design choices

There is no single correct loop structure. Here are three common patterns:

Simple reactive loop

The model decides the next step from the latest state only.

Good for:

• small assistants
• support tooling
• basic retrieval and tool use

Plan-and-execute loop

The model creates a plan first, then executes tasks against it.

Good for:

• longer tasks
• workflows with clear subtasks
• tasks that benefit from order and decomposition

Reflective loop

The model critiques its prior output before continuing.

Good for:

• reasoning-heavy work
• code generation
• analysis tasks where self-checking improves quality

Cost matters

Every loop step can add:

• one more model call
• one more tool call
• one more source of latency
• one more chance to fail

That is why agent engineering is not only about quality. It is also about controlling the number of loop iterations.

If an agent takes six model calls to do a job that a two-step workflow could do, that is usually a product decision problem, not only a prompt problem.

A production-minded example

Imagine an internal docs assistant:

1. Retrieve relevant pages from the knowledge base.
2. Ask the model whether the answer is already sufficient.
3. If not, reformulate the query and retrieve again.
4. Stop after two retrieval attempts.
5. Produce the final answer and cite sources.

That loop is limited, observable, and cost-aware. Those three traits matter more than sounding "advanced."

What to log

If you are building agent loops for real users, log at least:

• total steps
• tool calls by name
• model used per step
• latency per step
• total token usage
• final outcome

Without that data, debugging agents becomes guesswork.

Final takeaway

Agent loops are not complicated because the control structure is complicated. They are complicated because each extra iteration adds uncertainty, latency, and cost. Good loop design is mostly about restraint: give the model enough freedom to solve the task, but not so much freedom that it wanders.