---
title: "LangGraph | Skills We Assign For | Azendo"
description: "LangGraph for agent workflows — explicit state, cycles and human review, plus the Azendo roles assigned for it."
url: "https://azendo.co/skills/langgraph/"
---

[Skills](https://azendo.co/skills/) AI and machine learning 

# LangGraph.

LangGraph builds language model applications as explicit state graphs: nodes perform work, edges define transitions, and the graph holds state. It suits workflows with cycles, branching and human intervention points.

## Where LangGraph fits on a long engagement.

Making control flow explicit is the point. A chain that runs straight through is easy to express anywhere; a workflow that loops until a condition is met, branches on a model decision, or pauses for human approval needs structure, and a graph is an honest representation of that rather than a sequence pretending to be one.

Persisted state is what makes long-running and interruptible workflows practical. A process can stop, wait for a human, and resume days later with its context intact — which is the difference between an agent demo and something a business can put in a real process.

## What an assigned team does with LangGraph.

Agent systems need hard limits. Without maximum iterations, cost ceilings and timeouts, a loop that fails to converge will consume budget until someone notices, and "someone notices" is not a control.

Designing those bounds before deployment is a basic safety requirement, and it sits within the delivery standards applied under [ai engineering services](https://azendo.co/services/ai-engineering/).

## What we use LangGraph for.

* Workflows that loop and branch Control flow expressed as a graph rather than hidden in a sequence.
* Human approval inside the process Execution paused and resumed, so a person can review before it continues.
* Hard bounds on execution Iteration and cost limits, so a non-converging loop cannot run unchecked.

## How LangGraph capacity is assigned.

Agent workflow capacity is assigned with execution bounds and human checkpoints designed in rather than added after an incident.

## Roles we assign LangGraph for

* [AI Automation Engineer AI automation engineering](https://azendo.co/services/ai-automation-engineering/ai-automation-engineer/)

## Service lines it sits in

* [AI automation engineering](https://azendo.co/services/ai-automation-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in ai and machine learning

* [PyTorch — skill we assign for](https://azendo.co/skills/pytorch/)
* [TensorFlow — skill we assign for](https://azendo.co/skills/tensorflow/)
* [scikit-learn — skill we assign for](https://azendo.co/skills/scikit-learn/)
* [Pandas — skill we assign for](https://azendo.co/skills/pandas/)
* [NumPy — skill we assign for](https://azendo.co/skills/numpy/)
* [embeddings — skill we assign for](https://azendo.co/skills/embeddings/)
* [function calling — skill we assign for](https://azendo.co/skills/function-calling/)
* [vLLM — skill we assign for](https://azendo.co/skills/vllm/)

## Tell us what your roadmap needs LangGraph for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)