dfinke outlines a three-part framework for AI collaboration

dfinke lays out a conceptual framework for AI collaboration centered on three elements: State, Conversation, and Intent. His core argument is that AI teammates need all three in view at once to understand what a system is actually doing, why it changed, and what people are trying to achieve. He presents this not as a product launch, but as a way to think about how AI collaboration infrastructure could close the loop.

The three-part framework

In his definition, State is the ground truth of what is actually deployed and running. He says MCP can connect to and even reconfigure infrastructure such as Bicep, Terraform, and cloud resources, making this layer directly accessible to the system.

Conversation comes from day-to-day discussion in channels like Slack. According to dfinke, these discussions capture what broke, what changed, and why a decision was made. He treats that as a machine-usable reasoning layer.

Intent is the goal implied by what users say. He argues this can be captured directly from user expression, without first translating it into formal documentation.

Flywheel and implications

Dfinke describes the relationship among State, Conversation, and Intent as a flywheel. In his view, the three do not need to be centrally designed as one system; if each exists for its own practical purpose, they can naturally cross-check one another and absorb more of the cost of keeping context up to date.

He also raises a broader question: if MCP, Slack bots, and automation loops are increasingly converging and working well together, what changes about the necessity of humans in that system? The thread frames that as an emerging implication of this architecture rather than a settled conclusion.

2026-07-17 ~ 2026-07-17 · 6 related posts