I keep coming back to the agent loop when I am trying to figure out why a system misbehaves. The literature describes it as a single construct. The actual implementation has four separate phases, and a failure in any one of them looks like a failure in the whole thing.
The four phases are perceive, think, act, and remember. Every agent loop runs some version of this sequence. The differences between agents are in what each phase contains and how much work it does.
Why the loop has four phases and not one
The instinct is to treat the agent as a single inference call that produces an action. That model breaks down the moment the agent needs to call a tool, wait for a result, and then decide what to do next. A single inference call cannot wait. It cannot observe the world and then act on what it observed.
The loop exists because the agent needs a cycle it can repeat. Each cycle adds a little more to the task state. The loop terminates when the task is complete or when a stop condition is hit.
I wrote about how different agent architectures handle this loop in my post on the taxonomy of AI agents. Reflex agents skip the think phase almost entirely. Deliberative agents spend significant compute in think before acting. That structural difference matters more than most framework documentation suggests.
Perceive: what the agent sees before it decides
Perceive is the phase where the agent reads its current context and forms an internal picture of the task state. This is not passive. The agent actively selects what to attend to in the context window.
In practice, perceive means scanning the conversation history, any retrieved documents, tool results from the previous step, and the current task instruction. The agent builds a working representation of the situation.
The failure mode here is context contamination. If the context window contains stale information from a previous step, the perceive phase can lock onto the wrong picture. I have seen this happen when a tool returns an error that gets ignored or overwritten by subsequent steps. The agent perceives the error as resolved when it is not.
Structured logging in the agent harness is what lets you detect this. If you cannot see what the agent was attending to at each perceive step, you cannot diagnose why it made the wrong call.
Think: reasoning over the perceived state
Think is where the agent reasons about what it has perceived and decides what to do next. This is the phase that varies most between agent implementations. Some agents use chain-of-thought prompting and generate visible reasoning tokens. Others treat think as a latent computation inside the model weights.
The think phase is where the model evaluates whether it has enough information to act. It is also where the model decides which tool to call, if any, and with what arguments.
Here is what surprises engineers new to agent design: the think phase cannot be observed directly in most frameworks. You get the output of think, not the process. This is fine in happy-path scenarios. It becomes a serious debugging problem when the agent makes a reasoning error that is only visible twenty steps later.
Token budget management happens here too. The think phase consumes tokens. A deliberative agent that reasons extensively before acting will spend more per step than a reflex agent that acts on pattern matching. I covered token budget strategies in LLM token budgets and cost control. The think phase is usually where that budget gets eaten.
Act: calling tools and producing outputs
Act is the phase where the agent does something. That something might be calling a tool, returning a text response to the user, or updating internal state. Act is the only phase that produces observable side effects.
The act phase is where the loop interacts with the external world. A tool call passes arguments to an API and receives a result. The agent receives that result and feeds it back into the loop as a perceived input for the next cycle.
Tool schema design determines whether the act phase succeeds or fails. A poorly designed schema produces malformed arguments that the tool rejects. A well-designed schema with clear types and validation catches argument errors before the tool is even called. Tool schema design for reliability goes deep on what makes schemas actually work in production.
The act phase also has an implicit cost. Every tool call has latency. Some tool calls fail. The loop needs policies for both. Timeout enforcement and retry logic belong in the harness, not scattered in tool implementations.
Remember: updating state for the next iteration
Remember is the phase most agents get wrong. This is where the agent updates its internal state to reflect what just happened, so the next loop iteration has accurate context.
The simplest version of remember is appending the last tool result to the conversation history. That works for short tasks. It breaks for long ones.
The problem is that the context window has fixed capacity. Remembering everything means eventually forgetting something. The memory hierarchy in AI systems covers the layered approach that actually solves this. Short-term working memory lives in the context window. Long-term facts get written to external storage and retrieved when relevant.
The context windows vs memory post makes the case that conflating these two is how you build expensive systems that still lose track of what they were doing. Remember is not just appending. It is deciding what to keep, what to compress, and what to evict.
Checkpointing is the production version of remember. Writing state to durable storage after each successful act phase means the loop can recover from failures without restarting from scratch. This is not optional for agents that run longer than a few minutes.
Where it breaks down in practice
The loop sounds clean when described as four phases. It stops being clean when you actually run it.
Context window exhaustion happens in remember, not in perceive. By the time the agent cannot remember what it did ten steps ago, the problem was set in motion two dozen steps earlier. Teams that track context window usage in real time catch this before it produces incoherent outputs.
Infinite loops happen in act. The agent calls a tool, gets a result that looks like it needs more work, calls the tool again with slightly different arguments, and repeats. The stop condition never triggers because the agent keeps perceiving the task as incomplete. Setting explicit iteration budgets and testing them is not optional.
Tool result loss happens in remember. A tool call returns a result that the agent does not append to context. The next iteration proceeds as if the tool call never happened. This is usually a bug in how the harness handles tool results, not a model failure. Structured logging of every tool call and its result is the fix.
The production AI agent errors post has a fuller taxonomy of what goes wrong. The loop phases are the right frame for understanding those errors because each phase has its own failure modes.
The loop is a design tool
Once you see the four phases, you can evaluate any agent framework by asking what each phase contains and who controls it. Some frameworks give you fine-grained control over think. Others abstract it away. Some give you hooks in remember. Others hide state management entirely.
The frameworks that win in production are the ones where each phase is observable and controllable. You cannot debug what you cannot see. You cannot improve what you cannot measure. Breaking the agent loop into four phases gives you four places to look when something goes wrong.
Start with perceive. Check what the agent actually sees at the start of each iteration. Then check what it remembered from the previous iteration. Most agent bugs surface as perception or memory problems, not reasoning failures.