Codex vs Fable: Which AI Agent Picked the Better Problem?
This video presents a fascinating experiment comparing the AI agents Codex 5.6 and Fable in solving an open-ended problem: automating a business process. The core challenge was to have the AIs not just execute a task, but first discover the problem by analyzing real-world data (Slack, local files) and then proposing an automation solution. Fable demonstrated superior strategic problem identification, finding a more impactful and leveraged problem, while Codex opted for a more bounded, shape-focused approach. The video concludes by introducing a reusable Automation Discovery and Build skill designed to empower users to leverage AI for problem identification and robust automation creation across various platforms like Codex and Fable, emphasizing the importance of strategic problem-finding over tool selection.
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Nate B Jones presents a captivating comparison between Codex 5.6 (representing OpenAI’s agent capabilities) and Fable (an alternative AI agent), specifically focusing on their ability to solve an open-ended business automation challenge. The central premise of the experiment, termed "Find the job, then build it," was to task both AIs with discovering a recurring job within Nate's existing business processes and then automating it. This was a novel approach, as Nate explicitly instructed the AIs not to ask him which workflow to automate, but to identify three credible opportunities, choose one, and then build a controlled, safe, and repeatable automation without incurring additional costs or making unapproved external mutations.
The findings revealed a stark contrast in the AIs' strategic thinking and problem identification. Fable, despite being more challenging to set up due to multiple permission dialogues, ultimately excelled at strategic problem identification. After grinding through the data, Fable identified a problem that was much more interesting and leveraged. It recognized that one of the hardest jobs in the storytelling business (Nate's domain) is finding the right story to tell, i.e., determining what matters to talk about in a world of infinite AI stories. Fable proposed a "Slate Preflight" tool, a pre-greenlight duplicate and coverage checker for candidate topics. This tool surfaces nearest coverage and pipeline collisions, records deterministic results, and offers safe refusals, providing a powerful start gate for the content creation workflow. This demonstrated Fable's ability to understand the intent behind the prompt and discover a problem with significant strategic value.
In contrast, Codex (specifically in its "Ultra" setting, which allowed it to burn a tremendous number of tokens and gain extensive context from Nate's Slack and files) took a more narrow and shape-focused approach. Codex identified the problem of ensuring Content Package Handoff Preflight, a deterministic readiness check for completed content packages. This involved checking package shape and readiness, but not semantic meaning. While Codex successfully built a functional automation that was quick and efficient (one run, zero issues), Nate noted it was a typically Codex-flavored definition of the problem. It focused on the form of the problem rather than its deeper strategic implication. This highlights a key "con with Codex": when given an open-ended problem space, it tends to keep itself bounded, even with immense computational resources. It picks problems it can easily "wrap its arms around," focusing on readiness rather than semantic alignment.
Nate emphasizes that this is not a benchmark to declare one AI superior overall, but a case study revealing their differing strengths. Fable's strategic thinking is crucial for identifying high-leverage opportunities, guarding the start of the workflow. Codex, with its efficiency and dependability, is excellent for executing bounded tasks, acting as a handoff gate. The ideal scenario involves using both: Fable for discovery and strategic problem identification, and then potentially Codex for efficient and cheaper execution of the resulting automation.
To help others leverage these insights, Nate developed a reusable skill called "Automation Discovery and Build." This skill is designed to: 1) Map available work surfaces, 2) Observe repeated human glue (hidden work), 3) Qualify three candidates for automation, and 4) Build and prove one winner. The key takeaway is to "Ask the AI to find the job before naming the tool." This approach empowers users to uncover their own hidden automation backlogs—the "repeated human glue" appearing across chat (Slack), tracking (Linear handoffs), and artifacts (email and docs rework). By feeding their unique data fingerprints to an AI, users can gain a tailored automation solution, effectively creating a "magic easy button" for their business. This skill also incorporates safeguards, allowing users to define boundaries (like excluding personal Slack channels), ensuring responsible and controlled AI use. The ultimate goal is to leverage AI's non-deterministic nature to launch effective, automagic skills that audit unique user fingerprints, building neatly tuned automation solutions for the most pressing problems, while using cheaper execution engines (like Ringr, which Nate mentions as cheaper than Fable) for the final implementation.