LukeW

While chat interfaces to AI models aren't going away anytime soon, the increasing capabilities of AI agents are making the concept of chatting back and forth with an AI model to get things done feel archaic.

Let me first clarify that I don't mean open-ended text fields where people declare their intent are going away. As I wrote recently there will be even more broad input affordances in software whether for text, image, audio, video, or more. When I say chat AIs, I mean applications whose primary mode of getting things done is through a back and forth messaging conversation with an AI model: you type something, the model responds, you type something... and on it goes until you get the output you need.

Anyone that's interacted with an application like this knows that the AI model's responses quickly get lost in conversation threads and producing something from a set of chat replies can be painful. This kind of interface isn't optimal for tasks like authoring a document, writing code, or creating slides. To account for this some applications now include a canvas or artifact area where the output of the AI model's work can go.

In these layouts, the chat interface usually goes from being a single-pane layout to a split-pane layout. Roughly half the UI for input in the form of chat and half of it for output in the form of a canvas or artifact viewer. In these kinds of applications, we already begin to see the prominence of chat receding as people move between providing input and reviewing, editing, or acting on output.

In this model, however, the onus is still on the user to chat back and forth with a model until it produces their desired output in the artifact or canvas pane. Agents (AI models to make use of tools) change this dynamic. People state their objectives and the AI model(s) plans which tools to use and how to accomplish their task.

Instead of each step being a back and forth chat between a person and an AI model, the vast majority, if not all, of the steps are coordinated by the model(s) itself. This again reduces the role of chat. The model(s) takes care of the back and forth and in most cases simply lets people know when its done so they can review and make use of its output.

When agents can use multiple tools, call other agents and run in the background, a person's role moves to kicking things off, clarifying things when needed, and making use of the final output. There's a lot less chatting back and forth. As such, the prominence of the chat interface can recede even further. It's there if you want to check the steps an AI took to accomplish your task. But until then it's out of your way so you can focus on the output.

You can see this UI transition in the AI workspace, Bench. The first version was focused on back and forth instructions with models to get things done: a single-pane AI chat UI. Then a split-paned interface put more emphasis on the results of these instructions with half the screen devoted to an output pane. Today Bench runs and coordinates agents in the background. So the primary interaction is kicking off tasks and reviewing results when they're ready.

In this UI, the chat interface is not only reduced to less than a fourth of the screen but also collapsed by default hiding the model's back and forth conversations with itself unless people want to dig into it.

When working with AI models this way, the process of chatting back and forth to create things within in messaging UI feels dated. AI that takes your instructions, figures out how to get things done using tools, multiple models, changeable plans, and just tells you when it's finished feels a lot more like "the future". Of course I put future in quotes because at the rate AI moves these days the future will be here way sooner than any of us think. So... more UI changes to come!

The last two weeks featured a flurry of new AI model announcements. Keeping up with these changes can be hard without some kind of personal benchmark. For me, that's been my personal AI feature, Ask LukeW, which allows me to both quickly try and put new models into production.

To start... what were all these announcements? On May 14th, OpenAI released three new models in their GPT-4.1 series. On May 20th at I/O, Google updated Gemini 2.5 Pro. On May 22nd, Anthropic launched Claude Opus 4 and Claude Sonnet 4. So clearly high-end model releases aren't slowing down anytime soon.

Many AI-powered applications develop and use their own benchmarks to evaluate new models when they become available. But there's still nothing quite like trying an AI model yourself in a domain or problem space you know very well to gauge its strengths and weaknesses.

To do this more easily, I added the ability to quickly test new models on the Ask LukeW feature of this site. Because Ask LukeW works with the thousands of articles I've written and hundreds of presentations I've given, it's a really effective way for me to see what's changed. Essentially, I know what good looks like because I know what the answers should be.

The Ask LukeW system retrieves as much relevant content as possible before asking a large language model (LLM) to generate an answer to someone's question (as seen in the system diagram). As a result, the LLM can have lots of content to make sense of when things get to the generation part of the pipeline.

Previously this resulted in a lot of "kitchen sink" style bullet point answers as frontier models mostly leaned toward including as much information as possible. These kinds of replies ended up using lots of words without clearly getting to the point. After some testing, I found Anthropic's Claude Opus 4 is much better at putting together responses that feel like they understood the essence of a question. You can see the difference in the before and after examples in this article. The responses to questions with lots of content to synthesize feel more coherent and concise.

It's worth noting I'm only using Opus 4 is for the generation part of the Ask LukeW pipeline which uses AI models to not only generate but also transform, clean, embed, retrieve, and rank content. So there's many other parts of the pipeline where testing new models matters but in the final generation step at the end, Opus 4 wins. For now...

The last two weeks featured a flurry of new AI model announcements. Keeping up with these changes can be hard without some kind of personal benchmark. For me, that's been my personal AI feature, Ask LukeW, which allows me to both quickly try and put new models into production.

To start... what were all these announcements? On May 14th, OpenAI released three new models in their GPT-4.1 series. On May 20th at I/O, Google updated Gemini 2.5 Pro. On May 22nd, Anthropic launched Claude Opus 4 and Claude Sonnet 4. So clearly high-end model releases aren't slowing down anytime soon.

Many AI-powered applications develop and use their own benchmarks to evaluate new models when they become available. But there's still nothing quite like trying an AI model yourself in a domain or problem space you know very well to gauge its strengths and weaknesses.

To do this more easily, I added the ability to quickly test new models on the Ask LukeW feature of this site. Because Ask LukeW works with the thousands of articles I've written and hundreds of presentations I've given, it's a really effective way for me to see what's changed. Essentially, I know what good looks like because I know what the answers should be.

The Ask LukeW system retrieves as much relevant content as possible before asking a large language model (LLM) to generate an answer to someone's question (as seen in the system diagram). As a result, the LLM can have lots of content to make sense of when things get to the generation part of the pipeline.

Previously this resulted in a lot of "kitchen sink" style bullet point answers as frontier models mostly leaned toward including as much information as possible. These kinds of replies ended up using lots of words without clearly getting to the point. After some testing, I found Anthropic's Claude Opus 4 is much better at putting together responses that feel like they understood the essence of a question. You can see the difference in the before and after examples in this article. The responses to questions with lots of content to synthesize feel more coherent and concise.

It's worth noting I'm only using Opus 4 is for the generation part of the Ask LukeW pipeline which uses AI models to not only generate but also transform, clean, embed, retrieve, and rank content. So there's many other parts of the pipeline where testing new models matters but in the final generation step at the end, Opus 4 wins. For now...

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:

• Models are only as good as the context provided to them, making it crucial to ensure they have access to relevant information for specific tasks
• MCP standardizes how AI applications interact with external systems, similar to how the Language Server Protocol (LSP) standardized development tools
• MCP is not a protocol between models and external systems, but between AI applications that use LLMs and external systems
• Without MCP, AI development is fragmented with every application building custom implementations, custom prompts, and custom tool calls
• MCP separates the concerns of providing data access from building applications
• This separation allows application developers to focus on building better applications while data providers can focus on exposing their data effectively

• Two major components exist in an MCP system: client (implemented by the application using the LLM) and server (serves context to the client)
• MCP servers offer: Tools (functions that perform actions), Resources (raw data content exposed by the server), Prompts (show how tools should be invoked)
• Application developers can connect their apps to any MCP server in the ecosystem
• API developers can expose their data to multiple AI applications by implementing an MCP server once
• Allows different organizations within large companies to build components independently that work together through the protocol

• Tools should be simple and focused on specific tasks
• Comprehensive descriptions help models understand when and how to use the tools
• Error messages should be in natural language to facilitate better interactions
• The goal is to create tools that are intuitive for both models and users

• Remote MCP servers with proper authorization mechanisms
• An official MCP registry to discover available servers and tools
• Asynchronous execution for long-running tasks
• Streaming data capabilities from servers to clients
• Namespacing to organize tools and resources
• Improved elicitation techniques for better interactions
• There's a need for a structure to manage the protocol as it grows

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:

• Models are only as good as the context provided to them, making it crucial to ensure they have access to relevant information for specific tasks
• MCP standardizes how AI applications interact with external systems, similar to how the Language Server Protocol (LSP) standardized development tools
• MCP is not a protocol between models and external systems, but between AI applications that use LLMs and external systems
• Without MCP, AI development is fragmented with every application building custom implementations, custom prompts, and custom tool calls
• MCP separates the concerns of providing data access from building applications
• This separation allows application developers to focus on building better applications while data providers can focus on exposing their data effectively

• Two major components exist in an MCP system: client (implemented by the application using the LLM) and server (serves context to the client)
• MCP servers offer: Tools (functions that perform actions), Resources (raw data content exposed by the server), Prompts (show how tools should be invoked)
• Application developers can connect their apps to any MCP server in the ecosystem
• API developers can expose their data to multiple AI applications by implementing an MCP server once
• Allows different organizations within large companies to build components independently that work together through the protocol

• Tools should be simple and focused on specific tasks
• Comprehensive descriptions help models understand when and how to use the tools
• Error messages should be in natural language to facilitate better interactions
• The goal is to create tools that are intuitive for both models and users

• Remote MCP servers with proper authorization mechanisms
• An official MCP registry to discover available servers and tools
• Asynchronous execution for long-running tasks
• Streaming data capabilities from servers to clients
• Namespacing to organize tools and resources
• Improved elicitation techniques for better interactions
• There's a need for a structure to manage the protocol as it grows

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.

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.

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.

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.