PRDs, ADRs, skills, and context for AI agents

Teams often respond to unreliable AI output by writing more documentation. That can help, but it can also create a new problem: a long list of materials with no clear answer to what applies to the change in front of them.

PRDs, ADRs, skills, and project notes do different jobs. Treating them as one large prompt can blur those jobs instead of making the work safer to review.

What each artifact is good for

A product requirements document can explain the outcome a product change is meant to serve. It can capture user needs, scope, and acceptance expectations.

An architecture decision record can explain a durable technical decision: the boundary it establishes, why it matters, and what future work must respect.

A skill or operating guide can describe how a recurring activity should be carried out. It can keep a process consistent without pretending to decide the product outcome for every situation.

Project notes, designs, issues, and validation records can add evidence about the current state of the work.

Each is useful. None automatically answers all of these questions at once:

• Which sources apply to this specific change?
• Which statements are current, conflicting, or incomplete?
• What has an accountable human approved?
• What may an agent do, and what still needs a separate decision?
• What evidence should return after the work?

Context selects and exposes the boundary

For AI-assisted work, context should not mean “include every document.” A bounded Work Context is a focused package for one approved change. It points to the relevant sources, states the intended outcome and acceptance expectations, and keeps unknowns visible.

This is not a substitute for the underlying artifacts. The PRD still owns product intent. The ADR still owns durable architecture decisions. A skill still guides its recurring process. The context package makes their relevance to one change reviewable.

That separation prevents two common errors. First, an old or unrelated document does not quietly become a current instruction. Second, an agent does not receive authority merely because it can read a well-written document.

Authority and execution are separate

Even a careful context package does not authorize access, deployment, acceptance, or escalation. Those actions remain with the appropriate accountable humans and their existing processes.

The practical sequence is simple:

1. Define the outcome and name the accountable owner.
2. Select the relevant project sources and record gaps or conflicts.
3. Review one bounded Work Context.
4. Authorize execution separately if it is appropriate.
5. Review validation evidence and update the project context deliberately.

How Brainfab AI CTO uses these sources

Brainfab AI CTO keeps PRDs, ADRs, skills, project notes, and validation records in their proper roles. It selects the sources that apply to one change, exposes gaps or conflicts, and turns the approved direction into bounded coding-agent work.

Good artifacts become more useful when everyone can see why they apply, what they do not settle, and who remains responsible for the next decision. The AI CTO maintains that continuity; human authority remains explicit for consequential choices.