Combine task objectives with current information, model proposes operations.
Application calls available and permitted tools to read materials or generate files.
Check the actual results or errors, then decide how to proceed.
Agent: An AI system that advances tasks toward goals and decides next steps based on process results, typically combining models, tools, and execution environments. Think of it this way: it doesn't just generate a response—it attempts to advance tasks within the scope of its available capabilities.
For example, when preparing a lesson plan, it first reads the materials, notices that student prerequisites are missing, fills in those gaps, and then organizes the content. If a generated file fails to open, it continues handling the issue based on the error. How much it can advance and which steps need confirmation depends on the specific system.
Watching a Task Move Forward
This is a teaching illustration used to explain the process, not an actual operation log from any product.
| What happened | Reasonable next step | Available evidence |
|---|---|---|
| Received "Prepare a 40-minute Silk Road lesson plan for 7th grade" | Confirm materials and final deliverables | Clear requirements and available resources |
| Read materials, obtained M01–M03 | Organize the lesson plan based on materials | Actual content returned |
| Found example schedule totals 60 minutes | Adjust sections, recheck total duration | New time allocation and sum |
| File generation tool returned an error | Handle the error, or state what can only be delivered as text | Tool error and follow-up results |
| Requirements and deliverables verified | Deliver and note what still needs checking | Accessible materials and specific check results |
If reading materials fails but the response says "completed using the materials," the process results are not being used correctly. The task may need a different reading approach, additional materials, or to pause and wait for conditions—it cannot treat failure as success.
What Models, Tools, and Applications Do
The Model processes input and can propose next actions. The Tool executes specific operations, such as reading files or generating presentations. The AI App you open organizes these capabilities with the execution environment.
A model proposing "read course materials" doesn't mean it has system access. The application or execution environment must actually make the call, the relevant service must return results, and permissions must allow it.
In this site's map, these relationships should be read as: applications can provide Agent capabilities; Agents use models to determine next steps and call tools through the execution environment. These are responsibility relationships, not a fixed checklist of installed software.
Working with an Agent to Complete a Task
Provide the goal, relevant materials, allowed scope of operations, and completion criteria. Using this lesson preparation as an example:
Prepare a 40-minute lesson plan based on the specified materials. First check the grade level, student prerequisites, and whether materials are complete, then organize the content. Deliver the lesson plan text and two slides as a preview; if the ability to generate or view slides is not available, indicate the current completion status. Beyond ensuring files open and time totals are correct, also verify the material references and slide readability.
When a task has multiple stages, confirm key results before proceeding to later steps. For example, first finalize the lesson plan content, then create the slide preview—reducing repeated reformatting in the wrong direction.
Check actual deliverables and process evidence. "I have completed the check" is merely a statement; you should be able to point to which materials, files, or rendered pages were checked. Checks should also have reasonable scope—don't delay forever just because further modifications are possible.
Agent Does Not Mean Constantly Running Automatically
Applications with a chat interface may also provide Agent capabilities; having "Agent" in the name doesn't mean it will execute all operations. Whether a capability exists depends on tools, environment, permissions, and execution results.
Workflow is a sequence of steps with dependencies. Fixed workflows can specify "read materials → generate table → check"; Agents can decide next steps at stages requiring judgment. The two can be combined, and simple tasks may be handled by ordinary programs or a single conversation.
Skill provides reusable methods and materials; MCP specifies communication between applications and external services. Neither is a required installation for using an Agent.
Going Deeper: Why Tool Results Matter
A common execution loop is: consolidate current information → model proposes next step → environment executes → results obtained → continue, adjust, or end.
What execution returns may be data or errors. Subsequent decisions need to reference actual results; otherwise the entire process risks building on incorrect assumptions. Systems may also limit attempts, runtime, or scope of operations to prevent open-ended attempts without completion criteria. This is a common pattern and doesn't mean all Agent implementations are identical.
Think About It
An Agent has generated two slides but hasn't viewed any rendered pages, then says "no text overlap." What evidence is missing from this conclusion?
Reference judgment: Missing visual inspection of the actual output. Reading text, confirming file saved successfully, and confirming page layout is normal are different conclusions; pages need to be opened or rendered for inspection, issues found, then corrected.
Next Steps
First see the comparison of the four PPT capabilities, then learn about how Skills reuse methods or how MCP connects to materials as needed.
Sources and Scope of Application
Reference OpenAI Agents SDK and Anthropic Building effective agents, verified on 2026-09-09. The latter was published on 2024-12-19, and this site quotes its explanations of workflow, Agent, and environment feedback architecture—it is not used to describe current product operations. This site uses the teaching approach above: an Agent's autonomy and usage vary by application.