How PMs Design AI Features Users Actually Trust
For Product managers designing AI-powered features · Based on Swanepoel's Best Agents Four-Pattern Framework
// TL;DR
Product managers shipping AI features often see a pattern: initial excitement, then declining usage as users lose trust or find outputs too generic. Swanepoel's Four-Pattern Framework gives PMs a design checklist — Focus Modes, Transparent Execution, Personalization, and Reversibility — to spec AI features that earn sustained adoption. Use it to write better PRDs, evaluate vendor agents, and diagnose why an AI feature isn't getting the engagement you expected.
Why do users try my AI feature once and then stop using it?
The most likely reason is that your AI feature violates one or more of the four patterns that characterize the best agents. Users stop using AI features when outputs feel generic (missing Personalization), when they can't understand how the AI reached its conclusion (missing Transparent Execution), when they fear irreversible consequences (missing Reversibility), or when the feature tries to do too many things without excelling at any (missing Focus Modes).
As a PM, your first move should be auditing your AI feature against all four patterns. Score each as absent, partial, or present. The absent patterns directly explain your retention drop-off.
How should I write PRD requirements for an AI agent feature?
Structure your requirements around the four patterns:
Focus Modes: Define the 2–5 distinct tasks the AI feature will perform. For each, specify the expected inputs, the constrained tool set, and the user-facing framing. Don't ship a single undifferentiated mode — users need to know what to expect.
Transparent Execution: Require that the UI shows the AI's process, not just its result. Specify what visibility elements are needed: progress indicators, source citations, assumption disclosures, uncertainty flags. This is a UX requirement, not just an engineering one.
Personalization: Define what user context the AI should leverage — past interactions, team playbooks, connected data sources, stated preferences. Specify the litmus test: would the output look like the user's own work?
Reversibility: For every action the AI can take, specify the undo mechanism. Require confirmation gates for destructive actions. Define the granularity of rollback (line-level, document-level, action-level).
This four-pattern structure ensures your PRD covers the architectural requirements that determine whether users trust and adopt the feature.
How do I evaluate a vendor's AI agent using this framework?
Map the vendor's agent against all four patterns:
- Focus Modes: Does the agent have distinct modes for different task types, or is it a single do-anything interface? Ask the vendor how they evaluate quality per task type.
- Transparent Execution: Can you see what the agent is doing while it works? Can you see its sources, reasoning, and uncertainties? If it's a black box, push back.
- Personalization: Can you feed it your team's playbooks, connect it to your knowledge base, and does it remember past interactions? If every user gets the same generic output, it won't survive past pilot.
- Reversibility: What happens when the agent makes a mistake? Can you undo? At what granularity? If the answer is "just re-run it," that's not reversibility.
Vendors that score absent on multiple patterns will struggle to deliver production-grade value regardless of their model quality.
How do I prioritize which pattern to implement first?
Listen to your users and match their complaints to patterns:
| User complaint | Pattern to prioritize |
|---|---|
| "I don't trust the output" | Transparent Execution |
| "The results are too generic" | Personalization |
| "I'm afraid it'll break something" | Reversibility |
| "I don't know what it's good at" | Focus Modes |
Implement the highest-impact pattern first, measure the effect on usage and trust metrics, then move to the next. The framework is designed for incremental adoption — you don't need to ship all four patterns simultaneously.
Next step: Take your current AI feature's spec and score it against all four patterns. Share the scorecard with your engineering team and align on which pattern to prioritize in your next sprint.
// FREQUENTLY ASKED QUESTIONS
How do I measure whether Transparent Execution is working?
Track user intervention rates during agent execution (are users correcting the agent mid-process?), final output acceptance rates, and trust survey scores. If Transparent Execution is working, you should see users intervening earlier when the agent goes off track and accepting final outputs at higher rates. Also track support tickets related to 'I don't understand what the AI did' — these should decline.
What's the difference between Personalization and just letting users edit the prompt?
Letting users edit prompts puts the burden on the user to translate their preferences into prompt engineering. Personalization means the agent automatically incorporates the user's methods, past preferences, and organizational context without requiring manual prompt tuning. It includes Playbooks (team methods), Memory (learned preferences), and Connected Systems (user's data). The user's job is to provide context, not to engineer prompts.
Do I need Focus Modes if my AI feature only does one thing?
If your feature genuinely performs a single task type, you already have an implicit Focus Mode. Formalize it with a clear name, constrained tool set, and explicit user expectations. But check whether your single task actually has sub-modes — for example, a writing feature might benefit from separate Draft and Edit modes. Even two modes allow you to optimize and evaluate each independently.