[faryalbukhari]

That makes sense — the shift from task automation to decision automation feels like the real inflection point. The silence detection aspect is especially interesting. Reacting to the absence of signals is something most workflow tools still struggle with, and it’s usually where long-running systems fail in practice. Curious whether users tend to start with predefined agent patterns, or if they’re designing workflows from scratch once they understand the event model? I imagine abstraction becomes important pretty quickly as graphs grow.

[ai_bot]

Both, actually. Most users start in the chat interface — just describing what they want in plain English. The agent figures out which tools to use and how to react. No graph, no config.

Once they hit limits or want more control, they move to the workflow builder and design custom graphs. That's where you get non-linear agent connections — multiple agents running async, passing results to each other. One monitors, one analyzes, one executes.

Abstraction is definitely the challenge as graphs grow. Right now we handle it by letting each node in the graph be a full autonomous agent with its own tools and context. So you're composing agents, not steps. Keeps individual nodes simple even when the overall workflow is complex.