# Use Agent Profiles Efficiently

Agent Profiles control how your coding agents behave in different contexts.\
They define what the agent can read, plan, or execute — and how much autonomy it has.

To show how profiles change workflow, we’ll build an NFL Predictor App using two profiles:

* Strategic Agent
* YOLO Agent

{% embed url="<https://youtu.be/iD0R-8fY-tY?si=roCh6d78AuENAyFt>" %}

***

### Strategic Agent

Base Model: GPT-5\
Planning Model: Claude 4 Opus

Configuration:

* Apply code diffs → *agent decides*
* Read files → *always allow*
* Create plans → *always allow*
* Execute commands → *ask first*

When run:

1. The agent asks clarifying questions (e.g., *Do you want to scrape players and schedules?*)
2. Builds a detailed 14-step plan
3. Requests user input for environment variables

It’s thorough and safe — but pauses often if you miss setup details.

***

### YOLO Agent

Configuration:

* Apply code diffs → *always allow*
* Read files → *always allow*
* Create plans → *never*
* Execute commands → *always allow*

This agent skips long planning.\
It builds the project quickly, skipping over optional validation and focusing on essentials:

* Data ingestion
* Player stats
* Scoring calculation

It avoids brittle endpoints and produces a working dataset fast — though with fewer checks.

***

### Comparing the Two

| Trait     | Strategic Agent     | YOLO Agent         |
| --------- | ------------------- | ------------------ |
| Planning  | Detailed (14 steps) | Minimal (10 steps) |
| Safety    | High                | Low                |
| Speed     | Moderate            | Very fast          |
| Ideal For | Production projects | Quick prototypes   |


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.warp.dev/guides/configuration/how-to-use-agent-profiles-efficiently.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.