> For the complete documentation index, see [llms.txt](https://docs.warp.dev/llms.txt).
> Markdown versions of each page are available by appending .md to any URL.

# Configure YOLO and strategic Agent Profiles

Compare planning and command permissions with Strategic and YOLO Agent Profiles while building an NFL data pipeline.

Compare two [Agent Profiles](https://docs.warp.dev/agents/capabilities/agent-profiles-permissions/) while building a Python pipeline for NFL player statistics and game schedules. Use the Strategic profile to review plans and commands, then compare it with a more permissive YOLO profile in a disposable checkout.

Before starting, install Python 3.11 or later and review [creating and managing Agent Profiles](https://docs.warp.dev/agents/capabilities/agent-profiles-permissions/#agent-profiles). Both runs need network access to the public datasets. The core pipeline needs no secrets; provide any additional environment configuration before starting. More permissive command approval does not make generated code safe for production.

![Configuring YOLO and strategic Agent Profiles 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
    
    ```text
    Role & Goal
    You are my AI coding copilot inside Warp.
    Create a 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:
    An initialized repository with the CLI and ingestion modules.
    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
    
    Create or edit a profile for the first run using the [Agent Profiles controls](https://docs.warp.dev/agents/capabilities/agent-profiles-permissions/#agent-profiles). Enable planning and require command approval. The video uses GPT-5 as the base model and Claude 4 Opus for planning; choose models available to your account.
    
    | Action | Permission |
    | --- | --- |
    | Apply code diffs | Agent decides |
    | Read files | Always allow |
    | Create plans | Always allow |
    | Execute commands | Always ask |
    
    Use **Always allow** for reading files in both profiles so this comparison focuses on planning and command approval. Submit the project prompt in a new conversation with the Strategic profile selected.
    
    Review the [command denylist and auto-approve boundaries](https://docs.warp.dev/agents/capabilities/agent-profiles-permissions/#command-denylist); the table does not override those controls.
    
    In the recorded example:
    
    -   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.
        
    -   The run pauses on the invalid URLs instead of completing the pipeline.
        
    
    Clarifying questions help establish scope and storage choices before implementation. Missing environment variables or setup details can also interrupt a run; resolve those prerequisites before continuing.
    
3.  #### Configure the YOLO Agent
    
    Create or edit a second profile with these permissions, then start a separate conversation in a disposable checkout with the same project prompt. Keep credentials and production resources out of that checkout.
    
    | Action | Permission |
    | --- | --- |
    | Apply diffs / read files | Always allow |
    | Create plans | Never |
    | Execute commands | Always allow |
    
    In the recorded example:
    
    -   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 the failing schedule URLs, it focuses on player endpoints and completes a player-data pipeline.
4.  #### Compare outcomes
    
    Run the generated pipeline and repository checks. Inspect which datasets were actually downloaded, whether schedule data is missing, and whether output files and season summaries match the requested scope. Review generated code before reusing it.
    

| 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 |

These are observations from the video, not expected plan lengths or performance guarantees. Use explicit review checkpoints for production-bound work. A permissive profile can suit a disposable prototype, but it does not replace tests or human review.

## Troubleshooting

-   **Missing setup** - Provide required environment variables and dependencies before resuming. Do not print credential values into the conversation.
-   **Dataset URLs return 404** - Ask the agent to verify the source and report which requested datasets remain unavailable. A player-only dataset does not satisfy a request that also requires schedules.

## Next steps

The comparison produces a player-data pipeline and exposes the setup and source failures that still need review.

-   [Profiles & Permissions](https://docs.warp.dev/agents/capabilities/agent-profiles-permissions/) - Review permission boundaries before reusing a profile
-   [Review AI-generated code](https://docs.warp.dev/guides/agent-workflows/how-to-review-ai-generated-code/) - Check the generated changes and tests