The automation landscape is noisy, with every vendor promising “AI automation.” This article cuts through the hype to define the three distinct paradigms — traditional automation, AI automation, and AI agents — and explains when each delivers the best results.
GAR transforms enterprise analytics by merging AI reasoning with data verification. Built on a structured knowledge model, it delivers transparent, traceable, and conversational answers that empower faster, more confident business decisions.
Explore how AWS Bedrock Agents help solve the complex challenge of maintaining up-to-date AI model data at scale using open-source tools like Mistral.
Stop treating all tech debt equally. Learn how to identify and eliminate the technical constraints that are blocking revenue, deals, and innovation.
Why real AI transformation means evolving your systems — not just adding AI features. Discover why agent platforms are becoming the new enterprise core.
Avoid AI overkill (or underkill). This guide breaks down the 5 AI agent types every business leader should understand—and when to use each one for maximum impact.
Explore how MCP, A2A, and NANDA are setting new standards for scalable, composable, and interoperable AI agent infrastructure, ushering in the next era of the intelligent web stack.
Faster dashboards don’t mean faster discoveries. Discover why the key to true data science innovation is flexible, discovery-driven infrastructure—not just optimized reporting tools.
Unlock the power of anti-fragile AI: Build intelligent systems that grow stronger with disruption, not just survive it. Explore strategies, principles, and a roadmap for deploying adaptive AI in your organization.
This article provides a practical, strategic framework for maximizing ROI from Databricks. It outlines how to avoid the mistakes many organizations make with the platform and offers a 3-pillar approach -- optimizing infrastructure, accelerating workflows, and aligning teams -- to transform Databricks from a high-cost tool into a high-impact business asset.
As these organizations explore cloud options, a key consideration is how to integrate their existing VMware environments with cloud services without undergoing extensive re-platforming. Amazon Elastic VMware Service (EVS) is designed to address this need by allowing users to run their VMware workloads natively on AWS.
This blog introduces the Model Context Protocol (MCP), a new standard for enabling seamless collaboration between AI agents by unifying how they access tools and context. It explains how MCP breaks down integration silos, supports dynamic workflows, and fits into the growing ecosystem of AI interoperability protocols—paving the way for truly intelligent, multi-agent systems.
In today's relentlessly evolving business landscape, technology has decisively shifted from a mere support function to the very engine of business strategy and competitive differentiation. The pressure is immense: deliver value faster, pivot with market dynamics, and satisfy ever-increasing customer expectations. Businesses that can harness technology effectively will lead, while those that don't risk falling behind. This is where the concept of application modernization becomes not just relevant, but critical.
Unlock the full potential of AI process agents with strategic data access. No rip-and-replace needed—just smarter integrations and cross-functional visibility.
CloudGeometry CEO
:
"In today's rapidly evolving business landscape, understanding the fundamentals of AI Agents is no longer optional for technology, product and business leaders."
