Cloudelligent

Most AI agents forget context at the worst possible moment. For instance, a support agent resolves a billing dispute; the customer replies thirty seconds later, and the agent responds as if the conversation never happened. That gap is a common memory prob

AI agent development is rapidly moving up the abstraction stack. Building multi-tool agents used to require writing hundreds of lines of fragile orchestration code, managing state storage manually, and wrestling with custom infrastructure glue. While earl

Imagine a customer support agent processing two identical $50 refund requests within the same week. The first is approved in seconds through an automated workflow. The second escalates to a human manager or routes to a different tool instead. This diverge

AI code review is now becoming a checkpoint that every pull request must clear before it reaches production. AWS DevOps Agent's release management review pushes this further by reviewing changes for policy violations, dependency risks, and access-control

For the past year, Agentic AI has largely lived in proof-of-concept labs and executive slide decks. Today, it is moving onto the front lines of business operations. With a massive expansion of Amazon Connect, AWS is officially taking agentic AI beyond cus

What happens when an AI agent gives a polished, confident answer that sounds right but does not reflect how your organization works? This is one of the biggest challenges enterprises face when moving AI agents from demos into production. An agent may have

Traditional automation is cracking under modern operational pressure. Rigid, rule-based systems break the moment they hit real-time variations. Enterprise leaders face a brutal paradox where they must drive unprecedented efficiency and speed. However, the

The experimental phase of generative AI is officially over. We are moving beyond simple chatbots and into an era of autonomous, agentic workflows that act as genuine force multipliers for your business. Our latest insights from AWS Summit NYC highlight a

Every temporary environment you leave running becomes a form of technical debt that you pay for each month. For engineering teams, cloud waste rarely comes from one reckless decision. It is usually the byproduct of speed. Teams launch AWS resources to tes

If you look back five or six years, the typical Kubernetes cluster had a clear, predictable job. It was supposed to split a monolithic application into microservices, manage API routing, and ensure high availability. Success was measured in milliseconds o