# Configure YOLO and strategic Agent Profiles

> Configure custom agent profiles in Warp to control planning depth, autonomy, and execution speed — demonstrated with YOLO and Strategic examples.

- 网址：https://funcoding.ai/agents/warp/guides/configuration/how-to-configure-yolo-and-strategic-agent-profiles/
- 来源：Warp 官方文档原文（英文），MIT 许可，同步于 2026-10-11
- 官方原文：https://docs.warp.dev/guides/configuration/how-to-configure-yolo-and-strategic-agent-profiles/

---
<div class="callout callout-note">

This tutorial explains how **Agent Profiles** in Warp influence behavior, autonomy, and planning when coding with AI — demonstrated through the NFL Predictor app example.

</div>

1. #### Define the project

   I want to create an app that scrapes **NFL data** from the past decade, processes stats like team scores and player performance, and predicts future wins.

   The prompt specifies:

   * Data sources and constraints
   * Dependencies and CLI commands
   * Implementation details and deliverables

   ```
   Role & Goal
   You are my AI coding copilot inside Warp. 
   Create a production-ready Python project that ingests 2015–2025 NFL data to power future win projections. 

   Specifically: acquire week-level player and team stats, acquire game schedules + final scores (to determine weekly winners), and assemble a clean analytics dataset I can build models on later. Prefer stable/public data sources over brittle HTML scraping. Where scraping is unavoidable, respect robots.txt, add rate-limiting, and make scraping pluggable/optional. 

   Primary data sources:
   nflverse/nflreadr static files for weekly player stats and schedules (CSV/Parquet over HTTPS). 

   Tech constraints:
   Python 3.11+, no notebooks in the main flow. 
   Deterministic, idempotent pipelines. 
   Strong typing (pydantic) + docstrings. 
   Parquet as the storage format; small sample CSVs for quick checks. 
   CLI via Typer (warp run … friendly). 
   Logging (structlog), retry/backoff (tenacity), polite rate-limits. 
   Zero secrets required for core pipeline. 

   Deliverables:
   A fully initialized repo with the scaffold above.
   Implemented CLI + modules to download/ingest 2015–2025 data, compute/normalize fantasy PPR, produce winners by week, and write Parquet outputs. 
   One sample run in the README showing commands and example output counts. 
   If successful, run full 2015–2025. 
   Print a summary table (by season: games, players, weeks) at the end.
   ```

2. #### Configure the Strategic Agent

   **Base Model:** GPT‑5 (for reasoning)\
   **Planning Model:** Claude 4 Opus (for detailed breakdowns)

   | Action           | Permission    |
   | ---------------- | ------------- |
   | Apply code diffs | Agent decides |
   | Read files       | Agent decides |
   | Create plans     | Always allow  |
   | Execute commands | Always ask    |

   Behavior:

   *   The agent starts by asking clarifying questions:

       > “Do you want me to scrape both player stats and schedules or just one first?”\
       > “Where should raw data be stored — locally or in a database?”
   * It builds a **14-step plan** covering setup, dependencies, validation modules, and pipelines.
   * When the agent requests NFL schedule URLs, the chosen source returns 404 errors.
   * Execution halts — showing that the **Strategic** profile prioritizes verification over progress.

3. #### Configure the YOLO Agent

   **Permissions:**

   | Action                   | Permission   |
   | ------------------------ | ------------ |
   | Apply diffs / read files | Always allow |
   | Create plans             | Never        |
   | Execute commands         | Always allow |

   Behavior:

   * The YOLO agent skips detailed planning.
   * It produces a **10-step plan** that covers essentials only:
     * Initialize project
     * Build CLI
     * Ingest player data
     * Compute scores and transformations
   * Instead of using unstable schedule URLs, it focuses on reliable player endpoints — completing a functional data pipeline.

4. #### Compare outcomes

| Aspect      | Strategic Agent         | YOLO Agent                             |
| ----------- | ----------------------- | -------------------------------------- |
| Planning    | Detailed (14 steps)     | Minimal (10 steps)                     |
| Interaction | Clarifications required | Autonomous                             |
| Speed       | Slower due to checks    | Faster iteration                       |
| Output      | Stalled on invalid URLs | Working player dataset + summary table |