2025-05-22 08:00:00
In his AI Speaker Series presentation at Sutter Hill Ventures, David Soria Parra of Anthropic, shared insights on the Model-Context-Protocol (MCP), an open protocol designed to standardize how AI applications interact with external data sources and tools. Here's my notes from his talk:
2025-05-19 08:00:00
Anyone that's gotten into a long chat with an AI model has likely noticed things slow down and results get worse the longer a conversation continues. Many chat interfaces will let people know when they've hit this point but background agents make the issue much less likely to happen.
Across all our AI-first companies, whether coding, engineering simulation, or knowledge work, a subset of people stay in one long chat session with AI models and never bother to create a new session when moving on to a new task. But... why does this matter? Long chat sessions mean lots of context which adds up to more tokens for AI models to process. The more tokens, the more time, the more cost, and eventually, the more degraded results get.
At the heart of this issue is a technical constraint called the context window. The context window refers to the amount of text, measured in tokens, that a large language model can consider or "remember" at one time. It functions as the AI's working memory, determining how long of a conversation an AI model can sustain without losing track of earlier details.
Starting a new chat session creates a new context window which helps a lot with this issue. So to encourage new sessions, many AI products will pop up a warning suggesting people to move on to a new chat when things start to bog down. Here's an example from Anthropic's Claude.
Warning messages like this aren't ideal but the alternative is inadvertently raking up costs and getting worse results when models try to makes sense of a long thread with many different topics. While AI systems can implement selective memory that prioritizes keeping the most relevant parts of the conversation, some things will need to get dropped to keep context windows manageable. And yes, bigger context windows can help but only to a point.
Background agents can help. AI products that make use of background agents encourage people to kick off a different agent for each of their discrete tasks. The mental model of "tell an agent to do something and come back to check its work" naturally guides people toward keeping distinct tasks separate and, as a result, does a lot to mitigate the context window issue.
The interface for our agent workspace for teams, Bench, illustrates this model. There's an input field to start new tasks and a list showing tasks that are still running, tasks awaiting review, and tasks that are complete. In this user interface model people are much more likely to kick off a new agent for each new task they need done.
Does this completely eliminate context window issues? Not entirely because agents can still fill a context window with the information they collect and use. People can also always give more and more instructions to an agent. But we've definitely seen that moving to a background agent UI model impacts how people approach working with AI models. People go from staying in one long chat session covering lots of different topics to firing off new agents for each distinct tasks they want to get done. And that helps a lot with context widow issues.
2025-05-17 08:00:00
AI models are much better at writing prompts for AI models than people are. Which is why several of our AI-first companies rewrite people's initial prompts to produce better outcomes. Last week our AI for code company, Augment launched a similar approach that's significantly improved through its real time codebase understanding.
Since AI-powered agents can accomplish a lot more through the use of tools, guiding them effectively is critical. But most developers using AI for coding products write incomplete or vague prompts, which leads to incorrect or suboptimal outputs.
The Prompt Enhancer feature in Augment automatically pulls relevant context from a developer's codebase using Augment's real-time codebase index and the developer's current coding session. Augment uses its codebase understanding to rewrite the initial prompt, incorporating the gathered context and filling in missing details like files and symbols from the codebase. In many cases, the system knows what's in a large codebase better than a developer simply because it can keep it all "in its head" and track changes happening in real time.
Developers can review the enhanced prompt and edit it before executing. This gives them a chance to see how the system interpreted their request and make any necessary corrections.
As developers use this feature, they regularly learn what's possible with AI, what Augment understands and can do with its codebase understanding, and how to get the most out of both of these systems. It serves as an educational tool, helping developers become more proficient at working with AI coding tools over time.
We've used similar approaches in our image generation and knowledge agent products as well. By transforming vague or incomplete instructions into detailed, optimized prompts written by the systems that understand what's possible, we can make powerful AI tools more accessible and more effective.
2025-05-09 08:00:00
In his presentation Bridging AI and Human Expertise at UXPA Boston 2025, Stewart Smith shared insights on designing expert systems that effectively bridge artificial intelligence and human expertise. Here are my notes from his talk:
2025-05-09 08:00:00
In his Designing Humane Experiences: 5 Lessons from History's Greatest Innovation talk at UXPA Boston, Darrell Penta explored how the Korean alphabet (Hangul), created by King Sejong 600 years ago, exemplifies humane, user-centered design principles that remain relevant today. Here's my notes from his talk:
2025-05-09 08:00:00
In her Using AI to Streamline Personas and Journey Map Creation talk at UXPA Boston, Kyle Soucy shared how UX researchers can effectively use AI for personas and journey maps while maintaining research integrity. Here are my notes from her talk:
