The '/Goal' primitive in AI assistants like Codex is not a bigger prompt but a fundamentally different interaction. It defines a desired end state and success criteria, allowing the AI to loop, self-evaluate, and work autonomously until the 'contract' is fulfilled. This moves beyond the standard back-and-forth chat paradigm.
When Claude Code adopted the '/Goal' feature from Codex using the exact same name, it signaled an industry-wide recognition of a new, essential primitive for long-running AI tasks. This collaboration over competition suggests '/Goal' is becoming a foundational element of AI interaction, much like a standard command-line function.
Effective use of the '/Goal' feature requires a 'Goldilocks' scope. A goal that's too narrow ('fix this line') prevents the AI from finding root causes in dependencies. A goal that's too broad ('improve the system') makes the success criteria too vague for the AI to verify completion. The sweet spot allows for discovery within well-defined, verifiable boundaries.
To apply the '/Goal' primitive to non-coding tasks, knowledge workers should reframe their objective from finding a single 'answer' to producing a comprehensive 'audit.' This means the desired output is a verifiable ledger of what was checked, supported, contradicted, and unknown, with citations. This structure provides the clear, evidence-based finish line that a goal-oriented AI requires.
For many knowledge work applications of '/Goal,' such as vendor evaluation or candidate screening, an external, objective truth doesn't exist. The user must define the criteria for success by supplying a detailed, testable rubric. The AI's role shifts from finding information to applying the user's specific judgment criteria consistently across a large dataset.
The evolution of human-AI collaboration is moving up the stack of abstraction. What users manually coded as 'while' loops in 2024 and managed with prompt files in 2025 is now becoming a built-in product feature ('/Goal') in 2026. This trend simplifies agentic workflows, making them accessible to a broader audience by hiding the underlying complexity.