How To: Configure YOLO and Strategic Agent Profiles

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

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