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Core lesson

Agent Loops

Understand agent loops as bounded cycles of state, model decisions, tool calls, observations, and stopping rules in AI products.

4 min read

After this, you will understand

How Agent Loops helps you see how prompts, tools, retrieval, agents, and evals become real AI engineering workflows.

Article guideprerequisites, mental models, and concepts

Article overview

intermediateMechanicsAgentsReliability

Three useful mental models

In plain terms

Treat the idea as a definition to memorize.

Production pressure

Real systems force the idea to handle Agent Loops, State, and Planning.

Better reasoning

Use the concept to decide what the system guarantees, what it risks, and what it costs to operate.

Think before reading

Where would Agent Loops appear in a real production system, and what failure or bottleneck would it help you reason about?

As you read, look for the pressure that creates the idea first. The mechanics matter more once the reason is clear.

Connected learning

These lessons add useful context to the current core lesson.
  1. 1LLM EvaluationBuilding With AI

Concepts Covered

  • Agent loops
  • Task state
  • Planning
  • Tool calls
  • Observations
  • Stop conditions
  • Step limits
  • Human approval
  • Traces
  • Failure recovery

Definition

An agent loop is a repeated workflow where a model-backed system observes state, chooses a next step, uses an allowed capability, observes the result, and decides whether to continue.

The plain-English shape is:

state -> model decision -> action -> observation -> updated state

Then the loop either repeats or stops.

This is the difference between a single answer and an agent-style workflow. The system can use intermediate results to decide what to do next.

Why This Concept Exists

Some useful tasks cannot be solved by one model response.

For example:

  • fix a failing test
  • investigate a support ticket
  • gather facts from several systems
  • reconcile conflicting document evidence
  • draft a plan, execute a safe step, and inspect the result

The system needs feedback from the world between steps.

Agent loops exist because AI products increasingly combine model reasoning with software actions and observations over time.

The Beginner Mental Model

A beginner may think:

An agent is a model that keeps thinking until the task is done.

That hides the product architecture.

A safer mental model is:

controller manages loop
model proposes or selects next step
tools perform bounded operations
state records what happened
stop rules prevent endless or unsafe work

The loop is not only inside the model. It is an application workflow around the model.

Loop Anatomy

A practical agent loop usually has these pieces:

Goal: what the user or product is trying to accomplish.

State: what is known so far, including observations, partial work, constraints, and previous tool results.

Context assembly: the selected state and instructions sent into the model for this step.

Decision: the model proposes a message, tool call, plan update, or stop signal.

Action boundary: application code validates and executes allowed tools.

Observation: the result of the tool, user response, or environment change.

Stop condition: rules for completion, failure, approval, or escalation.

The model matters, but the boundary around the loop matters just as much.

A Concrete Example

Imagine a coding assistant asked:

Fix the checkout test failure.

A loop might run:

  1. read the failing test output
  2. inspect the source file mentioned in the error
  3. propose a small patch
  4. apply the patch
  5. run the targeted test
  6. inspect the result
  7. stop if the test passes, or revise if the failure changed

This is useful because each step updates the state.

It is also risky. A careless loop might edit unrelated files, run expensive commands repeatedly, or keep trying the same failed approach.

That is why the loop needs boundaries.

Stop Conditions And Guardrails

An agent loop should know when to stop.

Common stop conditions include:

  • the task is complete
  • required information is missing
  • the same failure repeats
  • the step budget is exhausted
  • the next action needs human approval
  • a tool returns a safety or permission error
  • confidence is too low for an automatic action

Without stop conditions, agent systems can waste cost, create confusing traces, or take actions beyond the user's intent.

State Drift

Long loops create state drift.

The system may summarize too aggressively, forget an earlier constraint, trust stale observations, or keep a wrong plan alive after new evidence arrives.

State drift is especially painful when the loop uses tools. A wrong assumption can become a wrong action.

Good agent design treats state as an explicit artifact:

what do we know?
what changed?
what remains uncertain?
what action is allowed next?

Observability

Agent systems need traces.

A useful trace shows:

  • what context was sent
  • what decision the model made
  • which tool was requested
  • what arguments were used
  • what the tool returned
  • why the loop continued or stopped

Without a trace, failures become hard to debug. The product team sees a strange final answer but cannot inspect the chain of decisions that produced it.

Common Confusions

An agent loop is not automatically autonomous.

The product can require approval, limit tools, or only allow read-only actions.

More steps do not automatically mean better results.

More steps can improve feedback, but they also add latency, cost, and failure surface.

Planning is not the same as execution.

A model can write a plausible plan. The loop still needs verified actions and observations.

Tool access is not permission.

The application must enforce whether a tool call is allowed.

What This Does Not Mean

Agent loops do not replace ordinary workflow design.

They still need idempotency, retries, timeouts, logging, permissions, rate limits, and user trust boundaries.

They also do not mean every AI feature should be agentic. Many tasks are better as retrieval, one model call, or deterministic software.

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Optional exploration

These links add context, but they do not replace the recommended next lesson.

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