Blogs

You approved the AI agent project. The demo looked great. Then the first invoice arrived. This is one of the most common moments in enterprise AI adoption right now. Teams build a working agent, ship it to production, and within 30 days they are looking at a cloud bill that is three to ten times higher than anyone projected. The agent is not broken. It is just expensive in ways that were never visible during testing. The problem is not the model. The problem is architecture.

AI systems have long been divided into those that reason and those that act. The ReAct framework collapses that divide — enabling LLMs to plan, retrieve information, and course-correct in real time using a structured Thought-Action-Observation loop.