AI agents are moving from demos to production. With LangGraph.js, you can orchestrate multi-step reasoning loops where an LLM plans, acts, observes results, and iterates — similar to how a developer debugs code.
Why LangGraph for agents
Unlike single-shot prompt chains, LangGraph models agent behavior as a state machine. Each node is a step (plan, execute, validate), and edges define transitions based on outcomes. This makes agents debuggable, testable, and safe to deploy.
A practical pattern: code execution agents
In my AiAgentRunJS project, the agent:
- Receives a task in natural language
- Generates JavaScript to solve it
- Executes code in a sandboxed environment
- Reads stdout/errors and retries if needed
This pattern works for data transforms, API glue code, and internal tooling.
Safety first
Production agents need guardrails:
- Sandbox execution — never run arbitrary code on the main process
- Token budgets — cap LLM calls per request
- Tool allowlists — restrict which APIs the agent can call
- Human-in-the-loop — confirm destructive actions
Combining with n8n
LangGraph handles reasoning; n8n handles scheduling, webhooks, and integrations. Together they form a powerful automation stack for teams that don't want to build everything from scratch.
Takeaway
Start with a narrow, well-defined agent (e.g. "transform this CSV schema"). Add tools incrementally. Measure success by tasks completed without human intervention — not by how impressive the demo looks.
