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.

As organizations explore more advanced uses of artificial intelligence, a critical distinction is becoming clear: not all AI systems function the same way. Two common categories—AI agents and agentic AI—reflect very different approaches to how machines assist with work. Understanding how they differ is necessary for developing long-term AI strategies that scale with the demands of modern enterprises. AI Agents: Capable Responders with Defined Boundaries An AI agent is an inte

Google’s Agent Development Kit (ADK) is an open-source framework that empowers developers to create intelligent AI agents integrated with Gemini models and Google Cloud. ADK simplifies the development of autonomous agents that can process tasks, make decisions, and interact with external systems, all with minimal coding effort. What is ADK? The Agent Development Kit (ADK) is a Python-based framework that allows you to design and deploy intelligent agents. These agents can exe