In 2015 a Google paper catalogued the ways machine learning systems rot, and a decade of MLOps tooling set out to pay that debt down. It paid down the debt that lives in pipelines and artefacts, and left the debt that lives in ju…
Most agent frameworks treat state as an afterthought, tacking memory onto a chain of LLM calls. LangGraph inverts the design: the graph is the state machine, every node transition is checkpointed, and cycles are first-class citiz…
A fraud model can score perfect recall offline and block nothing in production, because its training join looked a few hours into the future. Feature stores exist to enforce one contract: a training row may only see what the serv…
The industry frames every LLM feature as an agent. Most production systems that work are pipelines with one or two LLM steps and clear handoffs. Default to a workflow, reach for an agent only when the problem demands it.
The frameworks that demo best are rarely the ones that survive in production. LangGraph wins where it matters by refusing to hide the thing every other framework tries to hide: the control flow.
The model layer is commoditising and the answer flips every six months. The only durable advantage is the ability to A/B a model swap end-to-end in 48 hours and know whether it improved things for your users.