Every AI agent has a loop at its core: the model decides what to do, a tool runs, the result goes back to the model, and this repeats until the task is done. The question teams ask is whether to build that loop themselves or use an orchestration framework such as LangGraph. We use both, depending on the job.
What LangGraph gives you
LangGraph models an agent as a graph of steps with explicit state. That brings useful things: persistent state across steps, branching and loops you can see and test, built-in support for pausing for human approval, and the ability to resume a run after a failure. For long, multi-step workflows that is real value.
What a custom loop gives you
A custom loop is a few dozen lines of code in your own language: call the model, execute the requested tool, append the result, repeat with a step limit. It has fewer moving parts, no framework to upgrade, and any engineer on your team can read it in ten minutes.
When to choose LangGraph
- The workflow has multiple distinct stages with branching, for example classify, retrieve, draft, review, send.
- You need human-in-the-loop checkpoints that pause a run and resume later.
- Runs are long or must survive failures, so persisted state matters.
- You are coordinating several agents or sub-agents with shared state.
When a custom loop is better
- One task, a handful of tools, short runs. Most support-triage and lookup agents fall here.
- Your team will maintain it and wants minimal dependencies.
- You need tight control over latency and cost per call.
Our rule of thumb
Start with the simplest thing that meets the requirement, and move to a framework when the workflow's complexity, not fashion, demands it. Switching later is easier if tools, prompts and evaluation are kept separate from the orchestration code. Whichever route we take, the agent ships with an evaluation suite built from your real cases, permissions, logging and a hand-over runbook, because that is what makes an agent production-ready, not the framework.
If you are scoping an agent and want a view on the right architecture, see our AI agent development service or the broader guide to how AI agents work.
Frequently asked questions
Do you need LangGraph to build an AI agent?
No. Many production agents run on a simple custom loop. LangGraph is worth it for multi-stage workflows, human approval checkpoints, long-running or resumable runs, and multi-agent coordination.
Is LangGraph production-ready?
Teams run it in production, and its persistence and checkpointing suit long workflows. As with any framework, you should pin versions, test upgrades and keep your evaluation suite independent of it.
Can you migrate a prototype from one approach to the other?
Yes, especially if tools, prompts and evaluations are separated from orchestration code. That separation is what makes the choice reversible.
Related: Claude & OpenAI integration · Which model should power your agents
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