Separate conversational planning, codebase implementation, reusable skills, and controlled workflows.
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Trail 1
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Delegate bounded work with verifiable outputs while retaining product intent, risk acceptance, and final approval.
Trail 2
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Invest in prompt structure when the task is consequential, repeated, ambiguous, or difficult to review.
Trail 3
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Provide only relevant context, make hard boundaries explicit, and use examples to clarify rather than secretly replace requirements.
Trail 4
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Use them for speed when export, ownership, data handling, testing, and the path to deeper engineering review are acceptable.
Trail 5
Understand when to use a conversational assistant, a coding agent, a vibe-coding environment, or a multi-step workflow.
Trail 6
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Choose the surface according to whether the desired outcome is reasoning, a reusable artefact, or verified codebase change.
Trail 7
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Evaluate the tool by repository fit, permissions, review controls, team workflow, and quality of verification rather than brand preference.
Trail 8
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Use skills for repeatable work whose inputs, allowed actions, evidence, and failure handling can be stated clearly.
Trail 9
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Prefer inspectable, narrowly scoped skills from accountable sources and test them on low-consequence work first.
Trail 10
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Compare systems by control, observability, portability, permission boundaries, and recovery rather than the number of named agents.
Trail 11
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Resolve product choices before asking the agent to code, and separate confirmed requirements from assumptions it should surface.
Trail 12
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Ask whether another competent agent could identify the same target files and completion standard from the prompt alone.
Trail 13
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Test changed workflows, inspect high-consequence boundaries, and require reproducible evidence from the real runtime.
Trail 14
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Correct the smallest causal gap and preserve evidence of the failure rather than rewriting the entire prompt blindly.
Trail 15
What may the AI produce, what must it never decide, and what evidence lets a person approve the result? Set review triggers for sensitive data, money, permissions, migrations, complex integrations, incidents, and unfamiliar infrastructure.