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From Design to Decision: Unlock Deeper Insights with Video Recording for Prototype Testing

We’re excited to launch our video recording functionality for prototype testing, enabling you to dive deeper into the “why” behind user actions and empowering you to make data-informed decisions faster and with greater confidence.

See User Actions Come to Life


Capture the nuance of user interactions with screen, audio, and/or video recording. With Optimal’s video recording feature, you can:

  • Understand Intent: Watch users in action to reveal their decision-making process.
  • Spot Friction Points: Identify moments of hesitation, confusion, or frustration.
  • Test Your Ideas: Leverage user insights to make informed decisions before moving forward.
  • Track Task Success: Combine video insights with quantitative data to understand what works and what needs refinement.
  • Share Compelling Insights: Use recordings to drive alignment across your team and key stakeholders.

Drive Value with Video Recordings and Prototype Testing


By combining video recordings with prototype testing, you can unlock actionable insights that make a real impact. 

Here’s how they drive value for your initiatives:

  • Higher Conversion Rates: Optimized designs based on real user feedback lead to increased engagement.
  • Greater User Satisfaction: Tested prototypes help to better align your experiences with user needs and expectations.
  • Reduced Development Costs: Catch issues early to avoid costly fixes later in the development process.
  • Faster Time-to-Market: Resolve design flaws early to accelerate project timelines.

Recruit the Right Participants for Richer Results


Optimal combines the power of video recording, participant recruitment, and a comprehensive UX insights and research platform to elevate your product and research process.

Use Optimal’s recruitment service to quickly connect you with millions of people in 150+ countries ready to take part in your study. Our in-house team handles feasibility assessments, sends reminders and confirmations, reviews personalized study setups, and conducts human checks to ensure high quality participants to maximize the value of your video recordings.

Thank you, Beta Testers


We’re grateful to our early adopters and beta testers for shaping the future of video recording and prototype testing. Based on your valuable feedback, we’ve made the following updates:

Video recording updates

  • Additional recording controls: You can now control whether to reject participants or forward a participant to a non-recording study link if they do not meet your recording criteria. 
  • Translations: Set your study language and translate the recording instructions into 180+ languages.
  • No video expirations: We’ve removed video expirations, ensuring your recordings remain accessible as long as you have an active Optimal subscription.
  • Improved participant experience: We’ve improved the technology to reduce technical errors, creating a more reliable and user-friendly experience.

Prototype testing updates

  • Collapse/expand and move tasks: Increase prototype visibility by hiding or moving tasks, making it easier for participants to view and interact with more of your design, especially for mobile prototypes.
  • Option to end tasks automatically: When enabled, tasks will automatically end 0.5 seconds after a participant reaches a correct destination, removing the need for participants to confirm that they've completed the task. This can improve the overall participant experience, removing steps and making tests faster to complete.
  • Increased Figma frame limit:  We’ve increased the Figma frame limit from 30 to 100 frames to support larger, more complex prototypes.
  • Expanded task results: Task path results now indicated completed and skipped tasks for better analysis.
  • Time-saving improvements: Auto-select the starting screen after importing a Figma prototype, and enjoy task selection persistence across tabs in the analysis view.
  • Enhanced security: We’ve updated Figma authorization for expanded security for your prototypes.

Ready to unlock the power of video recording?
Get started with a prototype test
in Optimal or visit our help documentation to learn more.

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1 min read

Designing User Experiences for Agentic AI: The Next Frontier

Beyond Generative AI: A New Paradigm Emerges

The AI landscape is undergoing a profound transformation. While generative AI has captured public imagination with its ability to create content, a new paradigm is quietly revolutionizing how we think about human-computer interaction: Agentic AI.

Unlike traditional software that waits for explicit commands or generative AI focused primarily on content creation, Agentic AI represents a fundamental shift toward truly autonomous systems. These advanced AI agents can independently make decisions, take actions, and solve complex problems with minimal human oversight. Rather than simply responding to prompts, they proactively work toward goals, demonstrating initiative and adaptability that more closely resembles human collaboration than traditional software interaction.

This evolution is already transforming industries across the board:

  • In customer service, AI agents handle complex inquiries end-to-end
  • In software development, they autonomously debug code and suggest improvements
  • In healthcare, they monitor patient data and flag concerning patterns
  • In finance, they analyze market trends and execute optimized strategies
  • In manufacturing and logistics, they orchestrate complex operations with minimal human intervention

As these autonomous systems become more prevalent, designing exceptional user experiences for them becomes not just important, but essential. The challenge? Traditional UX approaches built around graphical user interfaces and direct manipulation fall short when designing for AI that thinks and acts independently.

The New Interaction Model: From Commands to Collaboration

Interacting with Agentic AI represents a fundamental departure from conventional software experiences. The predictable, structured nature of traditional GUIs—with their buttons, menus, and visual feedback—gives way to something more fluid, conversational, and at times, unpredictable.

The ideal Agentic AI experience feels less like operating a tool and more like collaborating with a capable teammate. This shift demands that UX designers look beyond the visual aspects of interfaces to consider entirely new interaction models that emphasize:

  • Natural language as the primary interface
  • The AI's ability to take initiative appropriately
  • Establishing the right balance of autonomy and human control
  • Building and maintaining trust through transparency
  • Adapting to individual user preferences over time

The core challenge lies in bridging the gap between users accustomed to direct manipulation of software and the more abstract interactions inherent in systems that can think and act independently. How do we design experiences that harness the power of autonomy while maintaining the user's sense of control and understanding?

Understanding Users in the Age of Autonomous AI

The foundation of effective Agentic AI design begins with deep user understanding. Expectations for these autonomous agents are shaped by prior experiences with traditional AI assistants but require significant recalibration given their increased autonomy and capability.

Essential UX Research Methods for Agentic AI

Several research methodologies prove particularly valuable when designing for autonomous agents:

User Interviews provide rich qualitative insights into perceptions, trust factors, and control preferences. These conversations reveal the nuanced ways users think about AI autonomy—often accepting it readily for low-stakes tasks like calendar management while requiring more oversight for consequential decisions like financial planning.

Usability Testing with Agentic AI prototypes reveals how users react to AI initiative in real-time. Observing these interactions highlights moments where users feel empowered versus instances where they experience discomfort or confusion when the AI acts independently.

Longitudinal Studies track how user perceptions and interaction patterns evolve as the AI learns and adapts to individual preferences. Since Agentic AI improves through use, understanding this relationship over time provides critical design insights.

Ethnographic Research offers contextual understanding of how autonomous agents integrate into users' daily workflows and environments. This immersive approach reveals unmet needs and potential areas of friction that might not emerge in controlled testing environments.

Key Questions to Uncover

Effective research for Agentic AI should focus on several fundamental dimensions:

Perceived Autonomy: How much independence do users expect and desire from AI agents across different contexts? When does autonomy feel helpful versus intrusive?

Trust Factors: What elements contribute to users trusting an AI's decisions and actions? How quickly is trust lost when mistakes occur, and what mechanisms help rebuild it?

Control Mechanisms: What types of controls (pause, override, adjust parameters) do users expect to have over autonomous systems? How can these be implemented without undermining the benefits of autonomy?

Transparency Needs: What level of insight into the AI's reasoning do users require? How can this information be presented effectively without overwhelming them with technical complexity?

The answers to these questions vary significantly across user segments, task types, and domains—making comprehensive research essential for designing effective Agentic AI experiences.

Core UX Principles for Agentic AI Design

Designing for autonomous agents requires a unique set of principles that address their distinct characteristics and challenges:

Clear Communication

Effective Agentic AI interfaces facilitate natural, transparent communication between user and agent. The AI should clearly convey:

  • Its capabilities and limitations upfront
  • When it's taking action versus gathering information
  • Why it's making specific recommendations or decisions
  • What information it's using to inform its actions

Just as with human collaboration, clear communication forms the foundation of successful human-AI partnerships.

Robust Feedback Mechanisms

Agentic AI should provide meaningful feedback about its operations and make it easy for users to provide input on its performance. This bidirectional exchange enables:

  • Continuous learning and refinement of the agent's behavior
  • Adaptation to individual user preferences
  • Improved accuracy and usefulness over time

The most effective agents make feedback feel conversational rather than mechanical, encouraging users to shape the AI's behavior through natural interaction.

Thoughtful Error Handling

How an autonomous agent handles mistakes significantly impacts user trust and satisfaction. Effective error handling includes:

  • Proactively identifying potential errors before they occur
  • Clearly communicating when and why errors happen
  • Providing straightforward paths for recovery or human intervention
  • Learning from mistakes to prevent recurrence

The ability to gracefully manage errors and learn from them is often what separates exceptional Agentic AI experiences from frustrating ones.

Appropriate User Control

Users need intuitive mechanisms to guide and control autonomous agents, including:

  • Setting goals and parameters for the AI to work within
  • The ability to pause or stop actions in progress
  • Options to override decisions when necessary
  • Preferences that persist across sessions

The level of control should adapt to both user expertise and task criticality, offering more granular options for advanced users or high-stakes decisions.

Balanced Transparency

Effective Agentic AI provides appropriate visibility into its reasoning and decision-making processes without overwhelming users. This involves:

  • Making the AI's "thinking" visible and understandable
  • Explaining data sources and how they influence decisions
  • Offering progressive disclosure—basic explanations for casual users, deeper insights for those who want them

Transparency builds trust by demystifying what might otherwise feel like a "black box" of AI decision-making.

Proactive Assistance

Perhaps the most distinctive aspect of Agentic AI is its ability to anticipate needs and take initiative, offering:

  • Relevant suggestions based on user context
  • Automation of routine tasks without explicit commands
  • Timely information that helps users make better decisions

When implemented thoughtfully, this proactive assistance transforms the AI from a passive tool into a true collaborative partner.

Building User Confidence Through Transparency and Explainability

For users to embrace autonomous agents, they need to understand and trust how these systems operate. This requires both transparency (being open about how the system works) and explainability (providing clear reasons for specific decisions).

Several techniques can enhance these critical qualities:

  • Feature visualization that shows what the AI is "seeing" or focusing on
  • Attribution methods that identify influential factors in decisions
  • Counterfactual explanations that illustrate "what if" scenarios
  • Natural language explanations that translate complex reasoning into simple terms

From a UX perspective, this means designing interfaces that:

  • Clearly indicate when users are interacting with AI versus human systems
  • Make complex decisions accessible through visualizations or natural language
  • Offer progressive disclosure—basic explanations by default with deeper insights available on demand
  • Implement audit trails documenting the AI's actions and reasoning

The goal is to provide the right information at the right time, helping users understand the AI's behavior without drowning them in technical details.

Embracing Iteration and Continuous Testing

The dynamic, learning nature of Agentic AI makes traditional "design once, deploy forever" approaches inadequate. Instead, successful development requires:

Iterative Design Processes

  • Starting with minimal viable agents and expanding capabilities based on user feedback
  • Incorporating user input at every development stage
  • Continuously refining the AI's behavior based on real-world interaction data

Comprehensive Testing Approaches

  • A/B testing different AI behaviors with actual users
  • Implementing feedback loops for ongoing improvement
  • Monitoring key performance indicators related to user satisfaction and task completion
  • Testing for edge cases, adversarial inputs, and ethical alignment

Cross-Functional Collaboration

  • Breaking down silos between UX designers, AI engineers, and domain experts
  • Ensuring technical capabilities align with user needs
  • Creating shared understanding of both technical constraints and user expectations

This ongoing cycle of design, testing, and refinement ensures Agentic AI continuously evolves to better serve user needs.

Learning from Real-World Success Stories

Several existing applications offer valuable lessons for designing effective autonomous systems:

Autonomous Vehicles demonstrate the importance of clearly communicating intentions, providing reassurance during operation, and offering intuitive override controls for passengers.

Smart Assistants like Alexa and Google Assistant highlight the value of natural language processing, personalization based on user preferences, and proactive assistance.

Robotic Systems in industrial settings showcase the need for glanceable information, simplified task selection, and workflows that ensure safety in shared human-robot environments.

Healthcare AI emphasizes providing relevant insights to professionals, automating routine tasks to reduce cognitive load, and enhancing patient care through personalized recommendations.

Customer Service AI prioritizes personalized interactions, 24/7 availability, and the ability to handle both simple requests and complex problem-solving.

These successful implementations share several common elements:

  • They prioritize transparency about capabilities and limitations
  • They provide appropriate user control while maximizing the benefits of autonomy
  • They establish clear expectations about what the AI can and cannot do

Shaping the Future of Human-Agent Interaction

Designing user experiences for Agentic AI represents a fundamental shift in how we think about human-computer interaction. The evolution from graphical user interfaces to autonomous agents requires UX professionals to:

  • Move beyond traditional design patterns focused on direct manipulation
  • Develop new frameworks for building trust in autonomous systems
  • Create interaction models that balance AI initiative with user control
  • Embrace continuous refinement as both technology and user expectations evolve

The future of UX in this space will likely explore more natural interaction modalities (voice, gesture, mixed reality), increasingly sophisticated personalization, and thoughtful approaches to ethical considerations around AI autonomy.

For UX professionals and AI developers alike, this new frontier offers the opportunity to fundamentally reimagine the relationship between humans and technology—moving from tools we use to partners we collaborate with. By focusing on deep user understanding, transparent design, and iterative improvement, we can create autonomous AI experiences that genuinely enhance human capability rather than simply automating tasks.

The journey has just begun, and how we design these experiences today will shape our relationship with intelligent technology for decades to come.

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1 min read

Get Reliable Survey Results Fast: AI-Powered Question Simplification

At Optimal, we believe in the transformative potential of AI to accelerate your workflow and time to insights. Our goal is simple: keep humans at the heart of every insight while using AI as a powerful partner to amplify your expertise. 

By automating repetitive tasks, providing suggestions for your studies, and streamlining workflows, AI frees you up to focus on what matters most—delivering impact, making strategic decisions, and building products people love.

That’s why we’re excited to announce our latest AI feature: AI-Powered Question Simplification. 

Simplify and Refine Your Questions Instantly

Ambiguous or overly complex wording can confuse respondents, making it harder to get reliable, accurate insights. Plus, refining survey and question language is manual and can be a time-consuming process with little guidance. To solve this, we built an AI-powered tool to help study creators craft questions that resonate with participants and speed up the process of designing studies.

Our new AI-powered feature helps with:

  • Instant Suggestions: Simplify complex question wording and improve clarity to make your questions easier to understand.
  • Seamless Editing: Accept, reject, or regenerate suggestions with just a click, giving you complete control.
  • Better Insights: By refining your questions, you’ll gather more accurate responses, leading to higher-quality data that drives better decisions.

Apply AI-Powered Question Simplification to any of your survey questions or to screening questions, and pre- and post-study questions in prototype tests, surveys, card sorts, tree tests, and first-click tests.

AI: Your Research Partner, Not a Replacement


AI is at the forefront of our innovation at Optimal this year, and we’re building AI into Optimal with clear principles in mind:

  1. AI does the tedious work: It takes on repetitive, mundane tasks, freeing you to focus on insights and strategy.
  2. AI assists, not dictates: You can adapt, change, or ignore AI suggestions entirely.
  3. AI is a choice: We recognize that Optimal users have diverse needs and risk appetites. You remain in control of how, when, and if you use AI.

A Growing Suite of AI-Powered Tools


The introduction of Question Simplification is just one example of how we’re leveraging AI to make research more efficient and effective for people who do research.

In 2024, we launched our AI Insights within our Qualitative Insights tool, summarizing key takeaways from interviews and transcripts. Now, we’re diving even deeper, exploring more ways to use AI to make research more efficient and effective.

Ready to Get Started? 


Keep an eye out for more updates throughout 2025 as we continue to expand our platform with AI-powered features that help you uncover insights with speed, clarity, and more confidence.

Want to see how AI can speed up your workflow?

Apply AI-Powered Question Simplification today or check out AI Insights to experience it for yourself!

Seeing is believing

Explore our tools and see how Optimal makes gathering insights simple, powerful, and impactful.