SiliconANGLE reported<\/a> that AT&T has nearly 500 agentic AI workflows already in production, and plans to triple that number next year. Luke Corbin, AT&T Business’s assistant vice president of digital transformation and AI, was speaking at an Amazon Web Services event in Boston earlier this month.<\/p>\nAccording to that reporting, AT&T processes about 45 billion AI tokens a day across the company, and its agentic platform handles more than 1 million transactions a month. The workflows range from customer provisioning to sales prospecting. Some of them now operate autonomously.<\/p>\n
The group behind it is called Business Agent Foundry, set up in early 2025. Instead of collecting isolated automation ideas, the foundry maps a whole business process, looks for unnecessary steps, handoffs, and dependence on old applications, and then redesigns the process around what agents can do. One early example involved AT&T’s mainframe computers: changes to customer fiber installation orders used to require employees to pull tickets from queues, update records by hand, and return them to the workflow. Agentic automation removed portions of that manual work. In another example in the report, AT&T halved the time needed to provision dedicated internet connections in buildings where fiber was already installed.<\/p>\n
The case for it<\/h2>\n
The strongest part of this story, to me, is not the number of workflows. It is the order of operations.<\/p>\n
AT&T did not, by this account, start by letting everyone build customer-facing agents however they wanted. Personal productivity agents are open to employees. Workflows that touch customers have to go through the foundry and through security, privacy, and compliance review. Every production workflow starts with human oversight. Only after accuracy reaches the mid- to high 90 percent range, within what the company considers a permissible risk profile, can an agent run autonomously. Then it stays under continuous monitoring for drift.<\/p>\n
That is a practical ladder: human in charge, human checking, autonomy earned, monitoring after. It treats autonomy as something a process qualifies for, not something a tool gets by default.<\/p>\n
I also like that the work started with process maps instead of prompts. A lot of failed automation I have seen keeps every bad step in a process and just makes the bad steps faster. Mapping first gives you a chance to delete a handoff or an old application dependency entirely. Deleting work beats automating work.<\/p>\n
And there is a real legacy problem here worth solving. Corbin pointed to the cost of maintaining old applications and training people to use them. When advanced software ends up talking to a mainframe so a fiber order can move without a manual queue in the middle, that is not a demo. That is the kind of unglamorous connection that actually saves time in a large company.<\/p>\n
The case against it<\/h2>\n
Now the caution, because this is still a company telling its own story at a vendor event.<\/p>\n
Every figure in that report \u2014 the nearly 500 workflows, the token count, the transaction count, and Corbin’s statement that production AI investments are achieving fivefold returns within a year \u2014 comes from AT&T itself. SiliconANGLE reported what Corbin said. That is not the same as an independent audit of what those workflows cost, how often they fail, or what the return calculation includes and leaves out.<\/p>\n
Tripling the count next year is a plan, not a result. A workflow count is also an easy number to grow and a hard number to judge. Five hundred small, well-scoped workflows and five hundred broad ones are not the same thing, and the report does not give me a breakdown I can weigh.<\/p>\n
There is also the accuracy threshold itself. Mid- to high 90s sounds high until you apply it to a process that touches a lot of customers. A process that is right 96 times out of 100 still gets it wrong regularly at scale, and the cost of the wrong cases depends entirely on what the workflow does. Provisioning help and sales prospecting are not the same risk as billing changes or service cancellations. The governance described in the article is a good sign, but the proof is in the error handling I cannot see from outside.<\/p>\n
Finally, redesigning processes around agents creates a new maintenance burden while it removes an old one. Business logic stored centrally, retrieval agents, planning agents, monitoring for drift \u2014 that is real infrastructure that has to be owned, updated, and understood after the foundry team moves on. Corbin himself said the foundry should not exist in the long run and its work should be distributed through the organization. I agree with the goal. Distribution is also where a lot of good central programs lose their discipline.<\/p>\n
Where I land<\/h2>\n
I believe the direction, and I am reserving judgment on the numbers.<\/p>\n
If a company the size of AT&T can map a process honestly, remove steps before automating what is left, keep customer-facing work behind review, and make agents earn autonomy with measured accuracy and continued monitoring, that is a model worth copying \u2014 especially for anyone running older systems that are not going away this year.<\/p>\n
What I will not do is repeat the fivefold return or the plan to triple as if they were proven facts. They are claims from the company doing the work, reported on November 14, and they should be read that way.<\/p>\n
The useful lesson for the rest of us is smaller and more durable than the headline count: do not start with the agent. Start with the process, write down where a human must stay in charge, decide in advance what accuracy earns more freedom, and keep watching after you grant it. That discipline is the technology story here. The agents are just what it produces.<\/p>\n","protected":false},"excerpt":{"rendered":"
Most AI announcements I read are about a model. This one is about work. On November 14, SiliconANGLE reported that AT&T has nearly 500 agentic AI workflows already in production, and plans to triple that number next year. Luke Corbin, AT&T Business’s assistant vice president of digital transformation and AI, was speaking at an Amazon […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-212","post","type-post","status-publish","format-standard","hentry","category-technical-commentary"],"_links":{"self":[{"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/posts\/212","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/comments?post=212"}],"version-history":[{"count":1,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/posts\/212\/revisions"}],"predecessor-version":[{"id":213,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/posts\/212\/revisions\/213"}],"wp:attachment":[{"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/media?parent=212"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/categories?post=212"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/quentinpidcock.com\/content\/wp-json\/wp\/v2\/tags?post=212"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}