The demo trap
Agent demos are easy and misleading. A single happy-path run looks like magic; it hides that the agent has no memory of failure, no bound on cost, and no idea what to do when a tool returns something unexpected. Production is the set of all the runs the demo didn't show you.
State you can reason about
LangGraph's value is making the agent's control flow explicit — a graph of nodes and transitions instead of a prompt hoping for the best. That structure is what lets you add the things production needs: retries on the right nodes, human approval gates on the risky ones, and a persisted state you can inspect when something goes wrong.
Guardrails and evaluation
An agent without evaluation is a rumor. Before anything ships, we wrap it in a harness: fixed scenarios, expected outcomes, and a score that moves when the behavior regresses. Guardrails bound what the agent can do; evaluation tells you whether it still does it after the next change.
Humans on the critical path
For anything with consequences, the right answer is often a human gate — automation speed up to the decision, a person on the decision itself. It's the pattern behind our own Laneloop, and it's how you get the throughput of automation without shipping a call nobody signed off on.
Have something to ship?
Start with a fixed-price, two-week MVP — or scope a full build with a senior team.