# Codex vs Fable: Which AI Agent Picked the Better Problem?

## Executive summary

The video compares two advanced AI agents, Codex and Fable, on an open-ended challenge: identifying and automating a high-leverage problem within a business process. The core finding is that while both agents successfully generated automation ideas, Fable demonstrated superior strategic thinking by identifying a more impactful pain point (pre-pipelining ideas). However, the speaker emphasizes that the true breakthrough is not just the AI's ability to suggest a problem, but the development of an advanced 'auto magic button' skill. This skill allows users to guide the AI to audit complex business processes across multiple data sources and build a complete, secure automation solution.

## Key takeaways

- AI Agents Must Pick the Problem: The challenge for modern AI agents is moving beyond simply executing a given prompt or tool. The goal is to have the agent inspect a user's entire workflow (e.g., local files, Slack) and autonomously define the most valuable problem requiring automation.
- Codex vs. Fable Performance: Codex was noted for being fast, dependable, and completing tasks successfully by picking a bounded, safe problem (e.g., improving handoff packages). Conversely, Fable demonstrated superior strategic sense, identifying a higher-leverage opportunity related to pre-pipelining ideas.
- The Need for Strategic Automation Skills: To solve the 'open claw problem' (knowing what automation is needed), a specialized skill is required. This skill guides the AI to audit complex business processes, understand multiple levels of causation, and recommend not just a fix, but a complete, secure tool.

## Technical details

- AI Agent Capabilities: The speaker highlights the difference between agents that merely execute tasks and those capable of strategic discovery, which is crucial for identifying true business pain points.
- Codex Ultra Mode: This special setting allows Codex to process large amounts of data but warns the user that it consumes a high number of tokens. The agent tends to select bounded, easily solvable problems.
- Pre-pipelining Concept: Fable suggested building an automation skill for 'pre-pipelining,' which helps refine and choose ideas—a strategic concept with high leverage in the storytelling business.
- The Auto Magic Button Skill: This proposed framework allows users to put safeguards (e.g., walls around private Slack data) and directs the AI to perform deep root cause analysis, leading to a full automation recommendation.

## Practical implications

- When implementing automation, prioritize AI agents that demonstrate strategic discovery capabilities over those that only execute defined tasks.
- The concept of 'pre-pipelining' suggests a valuable workflow for refining and prioritizing business ideas before committing to development.
- Build engineers should look into developing skills that incorporate multi-stage auditing (root cause analysis) rather than single-step automation scripts, ensuring the solution is comprehensive and secure.

## Topics

AI Agents, Automation Strategy, Large Language Models (LLMs), Build Engineering, Process Optimization, Newsletter, Spotify Podcast

Source: https://www.youtube.com/watch?v=uCWKXIyvM_8