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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.

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Unlock the full potential of AI process agents with strategic data access. No rip-and-replace needed—just smarter integrations and cross-functional visibility.

AI agents are becoming practical tools that autonomously perform tasks, support decision-making, and adapt to business needs. By starting with focused, high-value use cases and ensuring strong data governance and human oversight, organizations can unlock real value while building long-term capability.

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A data-centric approach to AI prioritizes improving data quality over tweaking models or code. As AI shifts toward unstructured data like text and images, traditional tools fall short. Data and analytics architects can address these challenges using four key pillars: data preparation and exploratory analysis, feature engineering, data labeling and annotation, and data augmentation. These pillars enable the creation of high-quality, AI-ready datasets, enhanced by modern tools like automation, low-code platforms, and synthetic data generation for scalable, intelligent systems.

Explore the critical differences between AI agents and RPA. Learn their strengths, limitations, and how businesses can combine both to drive intelligent, scalable, and future-ready automation strategies.

This blog provides a practical roadmap for business leaders looking to adopt AI agents to streamline operations, enhance efficiency, and drive innovation. It covers key steps, including identifying opportunities for AI agents, selecting the right technology, deploying AI solutions, and ensuring long-term success through maintenance and ethical considerations. Whether you're just starting your AI journey or refining existing implementations, this guide helps businesses harness AI agents effectively while mitigating risks.

AIOps leverages AI to automate IT operations, reducing downtime by analyzing vast data streams and predicting issues. The next step, agentic systems, enables AI to autonomously resolve problems, but this raises concerns around trust, making explainable AI essential. Responsible AI ensures ethical, fair, and secure operations, establishing guardrails as autonomous systems gain prominence.

Data silos are the natural result of decentralized systems and tooling decisions that optimize for individual departments rather than the organization as a whole. Common entities like "client," "customer," or "user ID" often differ across departments, complicating data integration -- custom ETL (extract, transform, load) processes (read: spaghetti code) that are challenging to scale and maintain. It doesn't have to be that way.

Modernization is inevitable. You're never finished. If you didn't do it last week, you're going to need to do it next week. That said, the pace of software change is continuing to accelerate, but sometimes simpler is better.

ChatGPT and GenAI have upended content creation and interaction with customers. As "newness" wears off, we settle into a (reasonably) reliable and predictable trajectory. Organizations have gone from "let's see how this works" to "we need to make this work for us ASAP."  And now, GenAI opens the door to a bigger technology change: agentic systems.

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Data Integration has become a key focus for organizations aiming to unlock value from their rapidly growing data. Cloud-scale data stores – databases, file stores, and the range of big data types – have led many to adopt a data lake house platform, Snowflake and Databricks most prominent among the many options.