Day: April 6, 2025

What is a Foundation Model?

I recently moderated a panel on Foundation Models at the Founder’s Creative engineering summit. We started with a simple warm-up question for our audience: “What is a Foundation Model?” Surprisingly, only about 15% of the 100+ engineers in the room could answer. Even within that small group, the definitions varied widely. This experience, along with a similar one at a UC Berkeley paper reading session, highlighted a fundamental gap in understanding. What actually is a foundation model?

I recommend that you pause here and and think about how you define it before proceeding. Don’t forget to add it to the comments section below.

The term Foundation Model was introduced and defined in the 2021 report titled Foundation Models: Opportunities and Risks of Large AI Models from Center for Research on Foundation Models (CRFM) and Institute for Human-Centered Artificial Intelligence (HAI) at Stanford University. I have pulled out three relevant nuggets from the report:

A foundation model is any model that is trained on broad data (generally using self-supervision at scale) that can be adapted (e.g., fine-tuned) to a wide range of downstream tasks

On a technical level, foundation models are enabled by transfer learning and scale.

Foundation model designates a model class that are distinctive in their sociological impact and how they have conferred a broad shift in AI research and deployment.

Combined together, we have a very clear definition of foundation model, in terms of how it is created, its capabilities and impact:

• Massive Scale: Built with large number of parameters to capture intricate data relationships and trained on a very large corpus of data.

• Self supervised learning: Trained using self supervised learning and hence does not need human annotations at scale.

• Versatile: Can be adapted to new tasks through fine-tuning, prompt-based learning, or few-shot/zero-shot methods without full re-training.

• Impact: Causing a major and broad shift in multiple domains with real world impact on people from all walks of life.

The selection of the term “Foundation” was deliberate as well:

The word “foundation” specifies the role these models play: a foundation model is itself incomplete but serves as the common basis from which many task-specific models are built via adaptation. We also chose the term “foundation” to connote the significance of architectural stability, safety, and security: poorly-constructed foundations are a recipe for disaster and well-executed foundations are a reliable bedrock for future applications

Arguably, we can limit the definition to the capabilities and impact and say that

That is it, very simple and yet has a lot behind it. What do you think? Do you agree with this definition? Have you seen usage of the term in any other context?

Designing for AI: A Designer’s Guide to Building Trust, Adaptability, and Ethics

As artificial intelligence (AI) reshapes industries, product designers face the challenge of crafting user experiences (UX) that align with AI’s capabilities, evolution, and ethical implications. Designing for AI is always beyond visuals, it’s about creating trust, enabling collaboration, and ensuring adaptability while prioritizing ethics and making users in control. Here is a guide by a designer to design for AI-driven products, from regular systems to autonomous AI agents and assistants.

Core Principles for Designing AI Experiences

I always call AI is UX because AI is all about User Experiences. Here are some core principles of AIX.

1. Design to create Human Trust

Trust is always psychological and Trust is the foundation of AI interactions. Users must feel confident in the AI’s reliability without unknown or scary expectations.

• Be Transparent: Clearly communicate the AI’s capabilities and limitations on what it can do.

• Set Expectations: Use visual cues and confidence score so that users know how much they can realy upon the AI recommendations and to explain AI decisions.

• Avoid Too much of Anthropomorphism: Don’t make AI too human, its not real human and its just assistant software, not a replacement for human connection.

2. Design for Quick Iteration, AI Evolution, and Hyper Personalization

AI is evolving fast and user needs evolve rapidly, so designs must be adaptable with trends and useless.

• Build Modular Systems: Use flexible frameworks which are modular to accommodate updates and upgrading to newer stuff.

• Prioritize Scalability: Design is never done and its a living system, not a static User Interface, it needs updates and scale.

• Enable Personalization: Provide control to the user and lets user edit the outputs to match their expecations and support rapid iteration.

3. Design for Ethical Use

The big thing in AI is biases and it can be amplify easily, invade privacy, or hide ownership. Designers must mitigate these risks and consider while designing the system.

• Reduce Bias: Check the system AI responses and create feedback loops for users to flag issues and raise concerns.

• Data Privacy: Must be very transparent about data usage on where data is coming, where data is going and provide required details and provide control to the users.

• Showcase Ownership: The AI generated outputs should be defined well about the ownerships weather its uers, platform or proivude so there is now confusion and its very clear.

4. Design for Collaboration

AI should be always a partner or a assistant. It should not be a dictator or show authority, it should be a good collaborator to help the users accomplish their tasks.

• Enable to Refine: Allow users to adjust AI response outputs tweaking the tone, sliders or adjusting the values, regenerate with variations, etc.

• Foster Dialogue: Design interfaces for conversation and interaction and just consumption and implementation. This will fetch better results.

• Encourage Co-Creation: AI is a tool which learns based on its training and keep training the system so it learn better and serves your better.

Designing for AI Agents

AI agents are autonomous systems that perceive, decide, and act to achieve goals, from simple rule-based bots to complicated machine learning models. Poor design can lead to wrong responses, misinterpretations, biased outputs, or unpredictable behavior, risking trust and accuracy. Thoughtful design ensures agents align with human values, handle both positive and alternative use cases, remain transparent and unbiased.

Behavioral Attributes of Agentic AI

Agentic AI systems are defined by these 6 key attributes and they are are the foundational elements of Agnetic user experiences. There is a general agreement on the key attribute of the simplest form of an AI agent.

1. Goal-orientation — following given objectives.

2. Perception — understanding the environment and context.

3. Reasoning — ability to deduce.

4. Acting — do things. Either by itself or use “tools” to do it.

5. Learn and Adapt — ability to update its memory and improve reasoning.

6. Autonomy — a degree of independence and self-governance.

Agentic UX and Designing for Agents

Agentic UX focuses on intuitive, AI experiences for users interacting with autonomous AI agents building trust. Unlike traditional UX, it accounts for the dynamic, proactive nature of agents. Designing for Agents is all about building systems that ensuring seamless user interaction with these 3 major principles craft experience.

1. Agentic UX Principles

• Transparency in Autonomy: Clearly communicate actions on what its going to do and aligning with Goal-orientation and Autonomy.

• Contextual Feedback: Use Perception and Reasoning for relevant responses tying to Proactivity and provide details.

• User Empowerment: Let users override with contextual actions balancing Autonomy with user control to what user want to do.

• Proactive Assistance: Offer non-intrusive suggestions which are relavant ensuring Proactivity feels helpful.

• Adaptive Tone: Adjust style of tone of language so it could be personal ,professional or soft or harder, etc.

2. Designing for Agents

• Support Acting: Enable tool integration and provide required API’s and inform users of actions with transparency.

• Enable Learning: Build memory systems to track preferences and prioritize reflecting Learn and Adapt.

• Facilitate Perception and Reasoning: Provide required data inputs for context-aware responses.

• Design for Proactivity: Use predictive models to anticipate the necessity and predict accordingly.

• Balance Autonomy: Create permission settings for user oversight and balance between system and human.

3. Integration with AI Assistant Design

• Optimize Interaction Flows: Add Transparency and Proactivity where ever required and balance accordingly.

• Visualize Context: Support Learning by showing past interactions and data insights.

• Keep It Simple: Use Proactivity subtly like notifications, inline instructions and streamline with right Actions.

Conclusion

There is so many agents growing and AI agents is the the future of intelligent software products. Designing for AI is very tricky and it requires balancing trust, adaptability, and ethics. By aligning designs with AI’s capabilities, supporting rapid interaction and evolution, prioritizing ethical use, and fostering collaboration, designers can create great user experiences that empower users. For AI agents, Agentic UX and thoughtful system design ensure better autonomy, disrupts the user experience. This holistic approach of designing thoughtful AI Agents builds AI systems that are capable, ethical, and delightful to interact with, fostering trust and collaboration. This all will result in a greater AI expeirences that delight users.