Claude and Codex have a relatively new feature, `/goal`, that lets you set a completion condition and keep the AI running autonomously until it's met.

This capability exposes a real challenge with agentic AI and explains why its shifting the bottleneck to its assymetric capabilities in different stages and domains of value creation.

00:09 The new /goal capability, what it does and how it works

02:07 What the official examples reveal: all output, no outcome

03:02 The missing examples, what outcome-oriented goals would actually look like

04:00 Output vs. outcome: why the gap matters for AI impact

04:45 The real bottleneck, observability and closing the feedback loop

05:45 Live demo: setting an outcome-oriented /goal on a blog post

08:30 What the loop did, changes made, open items, what's next

Are your AI goals output-oriented or outcome-oriented? What are you doing to enable outcome-oriented goals in your AI sessions?

And as a final thought - What would happen if your human teams were empowered to seek a /goal?

Insights on Scaling AI from Activity to Impact

Join the Activity to Impact Conversation on Linkedin

Goal-Based Loop Engineering: From Spec-Driven to Outcome-Driven – https://yuvalyeret.com/blog/ai-agent-completion-goals-aim-at-outcomes

Claude's /goal Feature Just Exposed A Real Challenge With AI Agents

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