Topic
How the work actually gets wired together: agents, pipelines, and the automation patterns teams are putting into production.
The hard part of an AI support system is not the model. It is teaching the system when not to trust itself. Real-world intent classification tops out near 0.74 F1, so a bot that answers everything is confidently wrong on a meaningful share of tickets. Here is the build that ships instead, with the evaluation that makes it safe.
Read featureEveryone wants a chatbot on top of their own documents. The technique, RAG, works, but the part that decides whether it works is your data, not the model. Here is a readiness checklist, annotated with why each line is load-bearing and what breaks if you skip it.
Read featureThe 95 percent figure everyone quotes is an abstraction. Here is one failure with a name, a date, and a bill: a chatbot that invented a refund policy, a company that tried to disown it, and a tribunal that did not care. The post-mortem reads the same every time.
Read featureAgents reached production not because the models got smarter but because teams built the boring scaffolding around them. The proof is blunt: the same model that passes a task once fails most of the time you ask it to pass eight times in a row.
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