Agentic Interaction Patterns

Before introducing an agent, confirm that predictive or generative capabilities actually improve the experience compared to rules, search, or better UI.

• Use AI where personalization, prediction, or open-ended input unlocks new value for users.
• Prefer simpler, deterministic solutions when predictability, auditability, or transparency are the main requirements.
• Validate with users which jobs-to-be-done benefit most from an agent versus conventional interfaces.

Shape how much users trust the agent by being explicit about capabilities, limitations, and error cases—especially in high-stakes contexts.

• Describe what the agent is good at, what it struggles with, and where users should double-check results.
• Avoid promising certainty or "near-perfect" performance for tasks where model error is still common.
• Use microcopy, examples, and onboarding flows to calibrate user mental models of the system.

Introduce agentic features in terms of outcomes ("what this helps you do") rather than model architectures or technical jargon.

• Highlight time saved, quality improved, or new workflows unlocked by the agent.
• Reserve technical detail for help content or expert audiences who explicitly need it.
• Pair marketing claims with realistic examples of what the agent can produce today.

Assume the agent will sometimes be wrong and make it easy for users to correct or bypass bad outputs without losing progress.

• Offer clear undo, edit, or rollback controls when the agent acts on the user’s behalf.
• Let users quickly switch to manual workflows or human support when automation breaks down.
• Capture error cases as structured feedback to refine prompts, policies, or models.

Tune how broad or conservative the agent’s results should be based on product stakes and user tolerance for noise.

• Favor precision in high-risk domains where false positives are costly or dangerous.
• Favor recall when exploration and serendipity matter more than filtering out every weak result.
• Explain to users why they might see fewer, higher-confidence results versus a longer, messier list.

Make it clear what data the agent uses, why it is needed, and how users can change their preferences over time.

• Request permissions at meaningful moments rather than in a single, overwhelming consent wall.
• Give users a simple way to review and adjust what the agent can access and remember.
• Explain the tradeoffs: what improves when users share more data and what happens if they opt out.

Let new users test the agent with low-commitment actions before making big decisions or sharing sensitive data.

• Provide sandbox or demo modes where users can see sample outputs with minimal setup.
• Favor reversible actions (drafts, suggestions, previews) before fully-automated changes.
• Offer piecemeal undo options so users can adjust preferences without a full reset.

Treat every interaction as an opportunity to learn from users. Make it easy to express approval, rejection, or corrections.

• Support lightweight feedback (thumbs up/down, hide, flag) directly on agent outputs.
• Acknowledge that feedback was received and, where possible, show how it influences future behavior.
• Combine qualitative reports with behavioral signals to refine prompts, routing, and safeguards.

Offer multiple levels of automation so users can start with guidance and gradually delegate more work to the agent as trust grows.

• Begin with recommendations or drafts that the user must explicitly confirm.
• Allow users to opt into higher automation levels (like auto-apply or scheduled actions) after they build confidence.
• Make it easy to dial automation back down or pause it when circumstances change.