Blog

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.

AI agents rely on a layered architecture: Data (storage & retrieval), Model (learning & decision-making), and Deployment (scalability & reliability). Developers must choose between open-source tools (flexibility) and commercial solutions (support & integration).

Key considerations include context management, prompt engineering, error handling, security, and scalability. AI agents are transforming customer service, sales, and software development, with future trends pointing toward specialized AI, proactive automation, and AI-assisted coding.

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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.

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.

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.

Transitioning from VMware to Kubernetes can feel overwhelming, but it doesn't have to be. Just like updating old furniture, you don’t need to throw everything out at once. This blog explores a practical, phased approach to modernization, helping you navigate from legacy systems to cloud-native infrastructure.

Kubernetes (K8s) and containers have become just about every developer’s bread and butter for building, deploying, and scaling applications. But let’s be real—using K8s in the cloud-native race isn’t always a walk in the park. In fact, even though K8s automates a lot of the heavy lifting, there are still plenty of ways to stumble.

In the fast-paced world of green energy, where the ability to adapt is crucial, Databricks provides them with the tools and flexibility they need to stay ahead of the curves in the supply and demand landscape.

In the world of enterprise data management, text-to-SQL technology, while helpful, is it simply not enough for today’s complex data environments?