Practical writing on agentic BPMN, AI workflow modeling, and building processes that don't break when your agents go sideways.
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Open the Editor →Four predictable failure modes that kill multi-agent coordination in production: race conditions, silent failures, handoff confusion, and escalation black holes. Here's how to diagnose and fix each one before they become incidents.
Read article →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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