What is an Agent Loop? Breaking down the perceive-plan-act-observe cycle
An Agent Loop escalates single-shot Q&A into autonomous task completion. The four steps (perceive, plan, act, observe) work cleanly in theory. In production, each fails differently: empty fields read as zeros, agents auto-approve unusual purchases at 3 AM, infinite loops consume tokens without stopping. This article examines what each step does, how it actually fails in production, and what kind of loop engineering prevents these failures.
By
Tenten AI 研究團隊
應用 AI
Published
March 2, 2026
Read time
6 分鐘
An Agent Loop is a cycle where an AI agent repeats four steps: perceive the environment, plan the next move, take action, observe the result. The loop continues until the task completes or a stopping condition triggers. This cycle transforms a single LLM call into autonomous multi-step task completion.
On paper this is clean. In practice, each of the four steps fails in its own way.
A manufacturing client hired us last year to build a procurement agent. It reads emails, cross-references quotation prices, and updates the ERP system. During the demo, three test cases all worked correctly. The manager approved it immediately. Nine days into production, the agent processed a quote with an empty unit-price field. It treated the empty cell as zero and auto-approved a purchase order for an abnormally large amount. No one designed this behavior. The agent simply executed its loop faithfully to an unexpected conclusion.
What is an Agent Loop: breaking down the four steps
To start, separate a single exchange from a full cycle. Ask ChatGPT a question and get an answer, that's single-pass inference. An agent works differently. It needs to complete multiple steps without instructions at each one. This requires a process that repeats.
Perceive: Feed the external state into the model. This could be user instructions, a database query result, output from the previous tool call, or a PDF.
Plan: The model decides what to do next based on current state. Which tool to call? What parameters to pass?
Act: Execute the action. Call APIs, write to the database, send emails, run code.
Observe: Get the result and determine if the task is complete. If not, treat the new result as fresh input and loop back to perceive.
When the loop turns smoothly, the agent appears intelligent. But each step fails separately when exposed to reality.
How each step breaks in production
| Step | Responsibility | Most Common Production Failure |
|---|---|---|
| Perceive | Convert world state into model-readable input | Dirty data treated as valid signal (empty field = 0), context overflow truncating critical info |
| Plan | Decide what to do next | Wrong tool selection, parameter hallucination, infinite loops bouncing between two steps |
| Act | Actually modify external systems | No permission boundaries, a single bad decision gets written to production, real money spent |
| Observe | Judge success or failure, decide whether to continue | Can't distinguish between "failed" and "done", either stops early or loops infinitely burning tokens |
Demos hide failures that surface in production. Demo input is clean, but production input is messy. The expected path differs from the actual one. In production, no one watches the system run at 3 AM. The perceive step never encounters the tidied data from presentations. The plan step faces branches you did not test in the demo.
The core problem usually is not insufficient model capability. It's a loop with no brakes and no way to see what's happening inside it. The agent has no way to know which iteration it's on, how much money it has spent, or whether the previous tool call actually returned an error.
Loop engineering: building the harness around the wheel
This is loop engineering. The model is just an engine. An engine requires a vehicle around it to function. That vehicle, which we call a harness, needs four essential components.
State management is the first requirement. Every input and output from each loop iteration must be recorded. The agent needs to know what it has already done and what remains. Without state tracking, it will reprocess the same order.
Stopping conditions are the second requirement. Define what completion looks like. Set iteration limits that register as timeout. Set token limits for forced stops. The infinite loop in the earlier example existed because stopping conditions were never clearly defined.
Guardrails and human checkpoints are the third requirement. Anything involving money, production data, or irreversible operations requires explicit confirmation or manual approval. The anomalous purchase order would not have happened if a gate had been added before the act step, something like: flag for human review if unit price is zero.
Observability is the fourth requirement. Log every plan decision, every tool call, and every returned result. Make these logs replayable. When something breaks, open the logs and understand what the agent was doing on iteration seven instead of confronting an opaque system.
None of these four things comes from the model. They require engineering work. This is why the real constraint in agent projects is not model selection. It's whether the team builds a solid frame around the loop.
When taking on a project, engineers visit the client site and work through actual messy data and genuine edge cases. They return and design each gate and stopping condition based on what they found. A working demo does not prove success. An agent succeeds when it does not fail in production systems and when actual users rely on it daily.
One stuck workflow
is enough to begin
Tell us what the team does today, where it breaks down, and what a better working day should look like.