---
topic: ai-technology
author: Crashtech Editorial
published: Nov 13, 2026 · read: 3 min
---

The Agent Loop in Plain Terms: Perception, Memory, Tools, Planning

Twenty agent buzzwords collapse into one loop: observe, think, act, check. ReAct, chain of thought, memory and handoffs, placed where they belong.

– –

Agent glossaries list twenty terms as if each were a separate invention. They are not. They are names for the parts of one loop, and once you see the loop, you can place every new buzzword in it within a few seconds.

The loop

          +----------------------------+
          |          GOAL              |
          +-------------+--------------+
                        v
   +--> OBSERVE  (perception, state, memory)
   |        v
   |     THINK   (reasoning, planning, chain of thought)
   |        v
   |     ACT     (tool call, message, handoff)
   |        v
   +--- CHECK    (observe result, evaluate, learn)

An agent keeps cycling until the goal is reached, it gives up, or a limit stops it.

Where each term lives

Observe what it knows
Environment: where the agent operates. Perception: reading inputs, documents or tool results. State: its current picture of the task. Memory: short-term (the context window) and long-term (stored outside the model and retrieved on demand). Knowledge base: the data it can look things up in.
Think how it decides
Reasoning and chain of thought: writing out intermediate steps before answering. Planning: breaking a goal into a sequence of actions. A simple reflex agent skips this and maps conditions straight to actions.
Act what it can do
Tools: APIs, search, code execution, databases. Action: any step it takes. Tools are what turn a model that talks into an agent that does.
Check how it improves
Evaluation: measuring whether actions worked. Learning loop: using feedback to improve, through better prompts, stored lessons or retraining.

The two patterns worth knowing by name

Chain of thought. Ask the model to show intermediate steps and multi-step problems get more accurate: arithmetic, logic, planning. It costs tokens and time, so skip it for simple lookups.

ReAct. Interleave reasoning and acting: write a short thought, call a tool, read the observation, think again. Because each step is anchored in real tool output rather than the model’s guess, it reduces invented facts compared with long unbroken reasoning.

Thought: I need the current price before I can compare.
Action: search("ACME laptop price")
Observation: $1,199 at store A, $1,149 at store B
Thought: Store B is cheaper. Check shipping.
Action: ...
Loops need brakes

An agent in a loop can burn money and time, or repeat the same failing step forever. Set a maximum number of steps, a budget, and a stop condition, and log every action so you can see what it did and why.

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Scaling past one loop

  • Orchestration: coordinating steps, tools or agents to complete a task pipeline.
  • Handoffs: passing responsibility from one agent to another, for instance from a triage agent to a billing specialist.
  • Multi-agent systems: several agents sharing an environment and collaborating.
  • Swarm: many simple agents following local rules without central control.
  • Agent debate: agents argue opposing views to improve an outcome.

Each of these multiplies the loop, and each handoff is a place for context to be lost or misunderstood.

Do

Start with one agent, a few well-described tools, a step limit and logging. Add agents when a clear boundary (different tools, permissions or expertise) justifies the handoff.

Don't

Reach for a swarm because it sounds advanced. A single agent with good tools is easier to debug, cheaper to run and often more reliable.

Takeaway

Forget the twenty-item glossary. Remember one loop: observe, think, act, check, repeat. Every term, from memory to ReAct to handoffs, is a name for one part of that loop or a way of stacking several loops. Place a new buzzword in the loop and you know what problem it claims to solve.

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Frequently asked questions

What is the ReAct pattern?

ReAct interleaves reasoning and acting. The model writes a short thought about what to do, takes an action such as calling a tool, reads the observation, and repeats. Grounding each step in real tool results reduces made-up facts compared with reasoning in a single unbroken chain.

What is chain of thought and when does it help?

Chain-of-thought prompting asks the model to produce intermediate reasoning steps before its answer. It tends to help on multi-step problems like arithmetic, logic and planning, where jumping straight to an answer is error-prone. It adds tokens and latency, so it is not worth it for simple lookups.

What is the difference between short-term and long-term agent memory?

Short-term memory is the current context: the conversation and recent tool results held in the prompt. Long-term memory is information stored outside the model, in a database or file, and retrieved when relevant, so the agent can carry knowledge across sessions without everything living in the context window.

When do I need multiple agents instead of one?

When tasks are separable, need different tools or permissions, or benefit from specialist prompts, and a handoff boundary is clear. Many systems work better as one agent with good tools. Add agents only when you can name the boundary and the reason, because every handoff adds failure modes.

Sources & further reading

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