Design Principles for Agentic Interfaces
Core principles for building trustworthy human-agent collaboration.
These principles adapt established AI UX guidance into an Agent Experience Design lens. Use them as guardrails when deciding how much to automate, how to communicate risk, and how agents should behave in real products.
User Autonomy
Design agents so people stay in control of important decisions. Automation should lower effort without trapping users in flows they cannot supervise, pause, or override.
- Automate more where stakes are low; keep humans in the loop where errors are costly or irreversible.
- Give users clear ways to supervise automation, review options, and take back control at any time.
- Introduce automation in phases (from suggestions → one-click actions → full automation) rather than all at once.
- Make it easy to recover when the agent fails: show what went wrong, what to do next, and how to continue manually.
Helpful, Honest Communication
Calibrate expectations by being clear about what the agent can and cannot do. Focus explanations on user value, not the underlying models.
- Set realistic expectations about accuracy, coverage, and failure modes instead of implying perfection.
- Explain benefits in plain language ("what this helps you do") rather than emphasizing the technology.
- Use explanations and, where appropriate, confidence cues to help users judge when to trust the output.
- Pair in-the-moment explanations with deeper, out-of-band education (onboarding, help center, or marketing content).
Accountable & Evolving Safety
Assume the agent will sometimes be wrong. Design for graceful failure, remediation, and continuous improvement rather than one-time safety checks.
- Identify likely error types and their impact on users before launch, then decide where you can safely automate.
- Provide fallback paths: human support, manual controls, or alternative tools when the agent cannot safely proceed.
- Let users easily report issues and see that their feedback is received and acted on.
- Review safety, policies, and interventions as the product, data, and usage patterns evolve over time.
Data & Model Alignment
Align agent behavior with real-world data, contexts, and domain expertise so the system behaves in ways users recognize as sensible and safe.
- Invest early in data quality: understand how training data differs from live data and how that impacts users.
- Embrace "real" data noise that reflects how users actually behave, rather than over-curating idealized datasets.
- Collaborate with domain experts and data labelers so labels, guidelines, and tools match real-world expectations.
- Actively maintain datasets over time and monitor for drift, gaps, and biases that degrade the experience.
Consent, Privacy & Comfort
Treat user data and comfort as first-class design constraints. Permissions, privacy settings, and agent behavior should all be understandable and revisitable.
- Clearly communicate what data is collected, why, and how it improves the experience.
- Make data and automation settings easy to discover, understand, and adjust over time.
- Let users try the system with limited commitments first, then deepen data sharing and automation as trust grows.
- Anchor the interface in familiar patterns so users can focus on understanding the agent’s behavior, not deciphering novel UI.
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