Generations of AI applications: conversational, delegative, and collaborative — /dev/knill<br>👾">
Disclosure<br>I work on AI token streaming at Ably.
Mental model<br>Walk into most product reviews, board decks, or “AI strategy” docs and the mental model on display is still the one from November 2022: a chat window, a back-and-forth, an LLM replying in prose. That model is two generations out of date, and teams building against it are solving the wrong problems.<br>Conversational<br>The conversational generation of AI applications came first. ChatGPT launched in November 2022, and through the first half of 2023 the Chat product category evolved. In early 2024 Google Gemini joined the race, and the Claude 3 family of models launched. These products are all part of the conversational generation of AI applications. It’s this generation of AI apps that still matches most people’s mental models. The core interaction of a conversational app is a text box at the bottom of the screen, you type a question or instruction, and the AI replies in the same window, in prose. This is also the design of most AI library examples. This is the design that uses HTTP request/response and SSE streamed responses. It’s the design that fits well into companies’ existing technologies and architectures. This mental model is closer to instant messaging than anything else, which is why some of the first areas of disruption were the areas where users were already interacting with a chat-box. Customer support, and search. In the conversational generation of AI applications, there’s no sense that the AI is doing anything for you. You are consulting the AI and it’s responding to you; answering your questions, asking you questions. Most people’s workflows operated on copy-pasting information in and out of the conversation. The AI’s response is essentially the whole product in the first generation of AI applications.<br>Delegative<br>The next generation is delegative . Where you delegate a task to the AI and it takes actions to fulfil that task. In Summer 2024 AI applications got the ability to call tools, and operate external systems became table stakes. There were many iterations of this through 2023 with GPT Actions, ChatGPT plugins, and Devin, but through 2024 we get the ability to build Artifacts (side-panel rendering of code, documents, diagrams), use Tools (being able to operate external systems), and MCPs (now the defacto standard for connecting AI to external systems). These product advancements changed the AI products from conversational to delegative. It started to feel like the AI was actually doing something, pulling in context from external systems over MCP, rendering documents that you could download and not just copy paste. We even saw the release of Computer use in late 2024; Claude Desktop was launched that could navigate a screen and take actions.<br>The big step for delegative AI applications came in 2025 when human relationship to the work shifted. AI products started to become agentic systems that humans delegate tasks to. Claude code launched in early 2025, and in the Claude 4 generation of models tool use became consistently good across long sessions. The models could reliably use and execute tools without going off the rails. This is the big shift from conversational to delegative; and it’s incredibly subtle. In the conversational era, humans were consulting the models to augment and improve their own work. Humans were still the executor, and the model was the assistant[1][1] You still see the ‘assistant’ naming in AI model APIs.<br>. In the delegative era, humans are now the supervisors who delegate work to agents. The agents take the actions based on the instructions or goal set by the human. The unit of work, and of value, shifts from a prompt and response to a task that the agent is fulfilling. This is how we see delegative applications today.<br>But delegative applications are also a lot harder to build than the original request-response architectures that engineers built for the conversational generation of AI applications. There are now long-running processes operating agentic loops, making tool calls, and performing multiple tasks over multiple turns. Each turn in the agentic loop can be potentially quite expensive, so engineers and architects start looking for mechanisms to make this agentic execution durable. Temporal and other durable execution frameworks start to take off, as they make the agentic loop durable, simplify the stateful aspect of the execution, automatically retry, and snapshot expensive computation or lookups.<br>Durable execution frameworks help with the computation, but they don’t help with the transport of the AI generated responses. As the agents become asynchronous long running processes, managing the connection between the agent and the client or human who made the request becomes a nightmare. The original HTTP request-response model...