Agent: Planning, Execution, Observation & Memory
If Tool Use addresses "whether a model can leverage external capabilities to do a single task," then an Agent addresses "whether a model can decompose multi-step goals, execute them incrementally, observe outcomes, and update its plan." An Agent is not a one-shot answer; it is a cyclical system architecture.
Define Goal First
Then Plan & Execute
Finally Refine via Observation
Switch Tasks to See How an Agent Works in Loops
After switching tasks, toggle the current stage. You will see what decisions the Agent makes, what capabilities it invokes, what results it observes, and how those results feed into the next planning cycle.
Current Goal
If Only a One-Shot Answer Were Given
Delivery After Agent Iteration
Why an Agent Is More Like a "System" Than Single-Turn Q&A
It Has State
A single-turn response usually ends after answering, but an Agent retains the current plan, completed steps, failure reasons, and intermediate results.
It Loops
An Agent does not "think through all steps at once." Instead, it acts, observes, and then decides the next step—much closer to real-world tasks.
It Often Uses Multiple Tools
A single task may involve search, databases, calculators, browsers, or code executors simultaneously, rather than just a single API.
It Relies Heavily on Guardrails
Because an Agent decides its own next actions, permission control, failure fallbacks, and observation validation are more critical than in standard Q&A.
The Five Steps of a Typical Agent Loop
1
Clarify Goal
Know exactly what needs to be delivered, not just how to answer a sentence.
2
Decompose Plan
Break complex goals into the most prioritized immediate steps.
3
Execute Action
Call tools, access systems, draft content, run commands, or search for information.
4
Observe Feedback
Update current judgment based on tool results, failure messages, or new evidence.
5
Update & Continue
Revise the plan, record progress, and continue until the delivery goal is met.
Common Misconception: An Agent does not become "automatically smart" just by wrapping it in a prompt. Reliable Agents typically require clear task boundaries, tool permissions, state management, and failure fallbacks working together.
Summary: An Agent transforms a model from "answering once" to "continuously acting, observing, and refining around a goal"—a crucial step toward integrating large models into real-world workflows.