# Source: https://docs.warp.dev/university/developer-workflows/power-user/how-to-configure-yolo-and-strategic-agent-profiles.md

# How To: Configure YOLO and Strategic Agent Profiles

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

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```
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.
```

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**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.
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**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.
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**Compare Outcomes**
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| 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 |