Inside the move from generative AI to agentic AI in enterprise finance
TL;DR
AT&T’s finance organization is building agentic AI workflows using LangGraph to automate manual journal entry preparation under SOX controls. The architecture separates repeatable preparation from human judgment through finance-owned playbooks, node-level audit evidence, and explicit approval boundaries.
Generative AI has already changed how companies draft, summarize and search for information. The next challenge is more complex: whether AI can coordinate work across business systems while preserving controls, auditability and human accountability.
That is the central test for agentic AI. Unlike a chatbot that returns an answer, an agentic system can interpret a goal, retrieve data, call tools, apply rules, validate results and prepare work for human review. In regulated functions such as finance, that capability creates both opportunity and risk. A useful system must do more than automate a task. It must show what data was used, what decision logic was applied, where exceptions occurred and who approved...
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