Practical writing on agentic BPMN, AI workflow modeling, and building processes that don't break when your agents go sideways.
No signup. No install. Drag tiles, connect flows, and simulate your multi-agent process — right in the browser.
Open the Editor →Agentic BPMN explained without the jargon. What it is, the three specific gaps in traditional BPMN, what agentic BPMN adds (decision gateways for LLM outputs, human checkpoints, agent handoffs), and a real multi-agent support flow walked through step by step.
Read article →Retry loops, ownership confusion, log-only debugging — these five signs tell you your agent orchestration has an invisible-complexity problem. Here's what each sign means and how a visual process map fixes it.
Read article →A practical guide to building visual process maps for AI agent workflows. The 5 building blocks — triggers, decisions, parallel paths, human checkpoints, and tool calls — with a step-by-step customer support workflow example.
Read article →Traditional BPMN tools like Camunda and IBM BPM were built for deterministic, human-driven processes. AI agents break every assumption. Here's what agentic BPMN adds — and when to use each approach.
Read article →A step-by-step guide to designing your first multi-agent AI workflow using visual boards — from mapping agent roles to connecting decision flows. No Python required.
Read article →Three approaches to AI agent orchestration — linear workflows, DAG pipelines, and visual boards. Each solves a different problem. Understanding the tradeoffs prevents months of rework.
Read article →Traditional BPMN was designed for humans following instructions. AI agents don't follow instructions — they reason, branch, fail unexpectedly, and hand off to humans mid-stream. Agentic BPMN is the modeling layer that bridges both worlds.
Read article →Stop describing workflows in Notion. Model them where they run.
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