Adobe Experience Manager (AEM) has introduced powerful new Agentic AI capabilities designed to continuously improve and adapt digital experiences at the speed of AI. By integrating advanced AI orchestrators through Agent-to-Agent (A2A) and Model Control Protocol (MCP) tools, AEM enables brands to automate complex workflows and enforce compliance seamlessly across enterprise ecosystems. Through a suite of specialized agents, teams can transition from manual, weeks-long processes into fast, AI-assisted workflows powered by simple natural language prompts. Photo by Tunahan KALAYCI via Pexels   Here is a breakdown of the key agents driving AEM’s new Agentic capabilities, their value propositions, their guardrails, and their current availability status. 1. Brand Experience Agent. Overview. The Brand Experience Agent accelerates digital modernization through specialized sub-agents—the Experience Modernization Agent, Experience Production Agent, and Experience Development Agent. Tog...

 There was a debate on whether to feature the term “AI” in the title of this article. Honestly, a key motivation for pursuing the research that led to this post was sparked by the widespread excitement about AI appearing constantly in our LinkedIn feed, to the point of feeling the fatigue, and even a bit disappointed in the algorithm of this, and the others, social media and content curated apps.  We soon discovered that there is an entire concept called "AI fatigue", not exactly how we were feeling it, but more about the mixed emotions people in the workforce have regarding the use of AI tools. Photo by Mart Production via Pexels (background updated with AI and Adobe  tech) From micro blog posts to video podcasts, lately, most of the tech content we encounter revolves around AI. They often sound or read very similar, usually mentioning the same few top providers. The articles (and social posts... at least the popular ones with paid-campaigns behind it) tend to focus less...

 Relationships have deteriorated, with trust diminished to an extent that may preclude restoration. The world, once structured to favour certain regions, has undergone significant shifts; for numerous countries, such advantages never existed. In this polarised reality, stakeholders are re-evaluating alliances, as former partners now often embody the role of "frenemy," thereby threatening freedom. This phenomenon is longstanding, rooted in historical power dynamics. When politics and influence supersede principles of fairness, respect, and integrity, ethical boundaries become blurred. Previously, issues that did not directly affect you would get overlooked out of principle, but current risks necessitate action to safeguard sovereignty. Information has consistently served as a key strategic asset, a trend only intensified by technological advancements that have elevated data as the principal factor. In other words, technology has amplified that, and data is the name of the game...

MCP servers are popular these days. We’ve been researching and exploring a few code repos, some where missing modularity, others just not having pieces that we were looking for… therefore we decided to build our own, simple and foundational that could be a starting point for those trying to solve for the similar things we were… and we decided to share it with the community, via our public github. MCP host, server,data sources     Before we start.  Using Typescript and NodeJS was one of our requirements. This proved somewhat challenging because I don't code as frequently these days due to my leadership responsibilities, and I typically prefer working with C# or Python. Colleagues in my tech community have been working with their teams on some of their MCPs going the Python route. Therefore, I said, “I guess we are trying the other route” 😊. One of our reasons to go with TypeScript was due to the need of the integration with APIs, and based on the research, it seems t...

The rapid rise of AI tools like ChatGPT, Gemini, and Perplexity is transforming how consumers interact with brands and make purchasing decisions. These tools are quickly becoming the go-to resources for research, leading to higher conversion rates and more informed buying choices. As traffic from AI technologies continues to surge, brands must adapt to stay relevant in this evolving landscape. Leaves falling - search box - Created with Adobe Express Enter Adobe’s innovative LLM Optimizer, an AI-first tool designed to help brands navigate the complexities of this new reality and ensure they capitalize on the benefits of AI-driven engagement. Here’s how LLM Optimizer can drive significant value for your brand through Generative Engine Optimization (GEO): 1. Gain Insights into Your Brand's Current Standing. LLM Optimizer empowers brands to understand their visibility in AI-driven search results. By providing comprehensive reports on current mentions, citations, and recommendations in ...

 Part I of this series, published in May, discussed the definition of MLOps and outlined the requirements for implementing this practice within an organisation. It also addressed some of the roles necessary within the team to support MLOps. Lego Alike data assembly - Generated with Gemini   This time, we move forward by exploring part of the technical stack that could be an option for implementing MLOps.  Before proceeding, below is a CTA to the first part of the article for reference. Assembling an MLOps Practice - Part 1 ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success... Take me there Components of your MLOps stack. The MLOps stack optimises the machine learning life-cycle by fostering collaboration across teams, delivering continuous integration and depl...

 These days, simply mentioning “AI” isn’t going to win anyone over. Clients expect authentic, data-driven results—not just bold claims or industry jargon. The expanding reach of AI brings some real concerns with it, such as errors, bias, and privacy risks are all in the mix. If those issues aren’t addressed, trust can erode quickly. It also affects an organisation’s dynamics, operations, and culture—regardless of size—including shifts in client relationships as expectations evolve. What can set a tech-consulting firm apart, among other things, is the dedication to building AI solutions on foundational values like fairness, transparency, accountability, privacy, security, and reliability. In other words, Responsible AI can be hard work, and it is a genuine differentiator. If an organisation can ensure the solutions implemented are ethical, clear, and consistently trustworthy, then it is likely it will foster customer confidence and loyalty. Photo by Google DeepMind from Pexels ...

In one of our previous articles it was highlighted how DevOps manages the End-to-End application cycle, leveraging agility and automation. CI/CD pipelines, collaboration and transparency, monitoring and automation are part of the list on how DevOps leverages and facilitates agility. What if then we bring those to support ML? That is how MLOps comes to the table and starts making sense! Lego Alike data assembly - Generated with Gemini A big tech corporation, or a startup, nowadays will see how it is becoming a requirement to incorporate AI and Machine learning (ML) in their operations. ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success.  What is MLOps Just to bring the definition of what you probably know (or put together based on the above) MLOps focuses on the life-cycle management of machine learning models. It combines machine learning with traditional ...