Beyond n8n for Workflow Automation: Agent Graphs as Your Universal Agent Harness

Original article published on March 20, 2025 Hardcoded multi-agent orchestration is brittle: topology lives in framework-specific code, changes require redeploys, and bottlenecks are hard to see. Agent Graphs externalize that topology into LaunchDarkly, while your application continues to own execution. In this tutorial, you'll build a small multi-agent workflow, traverse it with the SDK, monitor per-node latency on the graph itself, and update a slow node's model without changing application code. Node = AI Config (model, instructions, tools) Edge = handoff metadata (routing contract you define) Graph = topology (which nodes connect) Your app = execution + interpretation LaunchDarkly provides graph structure, config, and observability. Your application owns execution semantics: you write the code that interprets edges and runs agents. What You'll Build In this tutorial, you'll add Agent Graphs to an existing multi-agent workflow: Build a graph visually in the LaunchDarkly UI Connect i