When your average daily token usage is 8 billion a day, you have a massive scale problem.
This was the case at AT&T, and chief data officer Andy Markus and his team recognized that it simply wasn’t feasible (or economical) to push everything through large reasoning models.
So, when building out an internal Ask AT&T personal assistant, they reconstructed the orchestration layer. The result: A multi-agent stack built on LangChain where large language model “super agents” direct smaller, underlying “worker” agents performing more concise, purpose-driven work.