# How To: 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.

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.

![YouTube video](https://i.ytimg.com/vi/iD0R-8fY-tY/sddefault.jpg)

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 & GoalYou 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 |