The short answer
An AI agent works through a repeating loop, not a single response. It takes an input plus context (instructions, knowledge, prior conversation), reasons about the next step, picks a tool if it needs to act outside itself, executes, observes the result, then decides: loop again, stop because the goal is met, or ask a human before continuing a sensitive step.
The loop, step by step
1) Perception and input
The starting point: a message, an event, or a scheduled trigger. This is what kicks off the loop.
2) Context
Before reasoning, the agent is given context: role instructions, relevant knowledge from a knowledge base, and prior conversation history if any. Missing context is the most common cause of weak decisions.
3) Reasoning and planning
The agent evaluates the gap between the current state and the goal, and picks the next step that closes it. This evaluation repeats after every step, not just once at the start.
4) Tool selection
If the next step requires action beyond generating text — reading a record, sending an email, updating a status — the agent selects the right tool from a predefined toolset.
5) Execution
The tool is actually called with the parameters the planning step decided on.
6) Observation
The agent inspects the result of execution: did it succeed, fail, or return unexpected data? This observation feeds the next planning pass.
7) Iteration or stopping
Based on the observation, the agent decides: take another step, finish because the goal is met, or stop to ask for human approval at a sensitive or ambiguous point.
Loop stages table
| Stage | Question it answers | Example |
|---|---|---|
| Perception | What just happened? | A new customer message arrived |
| Context | What do I know about this situation? | Refund policy plus order history |
| Reasoning | What is the next step? | Check shipment status before replying |
| Tool selection | Which tool performs this step? | A shipment tracking tool |
| Execution | What actually happened? | The tool was called and returned a status |
| Observation | Is the result enough? | Status: delayed — needs another decision |
The role of memory and context
Memory is what connects one loop to the next across time: remembering a customer already complained, or that a ticket was escalated before. Without it, an agent re-asks the same questions every time. Context often comes from a knowledge base, and wiring an agent to one closely resembles retrieval in RAG-style systems (retrieval-augmented generation).
When an agent stops
- The stated goal for the task is met.
- A maximum number of steps or time budget is reached.
- It hits an irreversible action and requests human approval.
- It hits ambiguity it cannot resolve from available context.
Limitations of this model
This is a conceptual walkthrough of the loop, not a build guide. Turning this loop into an actual agent on WKIL means translating it into instructions, knowledge, tools and approval points configured in practice — that is what the how-it-works page covers, not this article.