For Workflows Too Branching for a Single Prompt
LangGraph lets us define a multi-step agent workflow as an explicit graph of steps and decisions — useful when a task genuinely branches, loops, or hands work between specialised steps. We reach for it when that structure earns its keep, not as a default for every build.
A Graph of Steps and Decisions, Not Magic
Instead of one large prompt trying to carry an entire multi-step task, the workflow is broken into discrete steps — each one doing a specific job — connected by explicit decision points that choose what happens next. That structure is what makes a complex workflow inspectable, testable and correctable, rather than a single black box.
Steps, Not One Big Prompt
Each step does one job — classify, retrieve, call a tool, draft, check — which makes it possible to test and fix a single step without re-testing the whole workflow.
Explicit Decision Points
Where the workflow branches is written down as a real decision in the graph — including the branch that routes to a human — not left implicit inside a paragraph of instructions.
Steps Can Loop Back
A step can request more information and return to an earlier point in the graph instead of failing outright — useful for tasks that genuinely need a second pass.
A Graph Earns Its Keep on Some Tasks, Not Others
Structure has a cost — more to design, more to test, more to explain. We use it when the workflow's shape justifies it, and say plainly when it doesn't.
Good Fit
A support case that might need a lookup, then an escalation check, then a different path depending on what's found. Several specialised steps handing work to each other in a non-linear order. A workflow with a genuine "go back and ask again" loop.
Usually Overkill
A fixed sequence — read, extract, file — with no real branching. A single answer-from-a-knowledge-base task. Anything where the honest workflow diagram is a straight line, not a graph.
The Graph Is the Scaffolding, Not the Whole Agent
LangGraph gives structure to how the steps connect. The engineering work is still the same underneath: deciding what each step is allowed to do, which systems it reads from and writes to, what counts as a clean handoff to a person, and testing the whole path against real and edge-case inputs before it touches production.
The model powering individual steps is a separate choice from the graph structure itself — see OpenAI Agent Development and Claude Agent Development for how we evaluate that.
See Our Engineering ApproachWe Test Against
Every branch in the graph, not just the most common path, including the ones that should end in a human handoff.
We Monitor For
Which branches actually get used in production, steps that loop more than expected, and cases that reach an unhandled path.
Related Reading
LangGraph is an orchestration choice, separate from which job the agent does or which model powers it.
Multi-Agent Systems
Several specialised agents handing work to each other — the platform-level version of the same idea.
AI Workflow Automation
Multi-step internal tasks completed across your existing tools.
AI Agent Engineering
How we approach evaluation, testing and monitoring, independent of the framework choice.
LangGraph Agent Questions
What is LangGraph, in plain terms?
A framework for defining a multi-step agent workflow as a graph of steps and decisions, rather than a single prompt trying to hold the whole task in one pass. Each step in the graph can call a tool, check a condition, loop back, or hand off to another step, which makes the logic easier to reason about and test than one long instruction.
Are you an official LangChain/LangGraph partner?
No, and we don't claim any formal partner or certification status. LangGraph is an open framework we build with because it fits a specific category of problem well — judge the fit from the work, not a badge on this page.
Is every agent you build on LangGraph?
No. A single-step task — answer from a knowledge base, extract a few fields from a document — usually doesn't need the overhead of a graph at all. We reach for LangGraph specifically when a workflow branches enough that a plain sequential design starts getting hard to follow or test.
When does a workflow actually need a graph structure instead of a simple pipeline?
When the path genuinely branches — different outcomes need different next steps, a step sometimes needs to loop back for more information, or several specialised steps need to hand work to each other in a non-linear order. If the task is really "do A, then B, then C" in a fixed order, a simpler design is usually the honest answer, not a graph for its own sake.
Does using LangGraph change what the agent can decide on its own?
No — the escalation boundaries are a design decision we make regardless of framework. LangGraph makes it easier to represent "hand off to a human here" as an explicit branch in the graph, which is a testing and clarity benefit, not a change to how conservative or permissive the agent is by default.
Tell Us What the Workflow Looks Like
We'll tell you plainly whether it needs a graph-based build or whether a simpler design gets you there faster.