Recent discussions on “agentic AI” have gained traction, highlighting its potential capabilities. At its core, agentic AI refers to systems that can: Set and pursue goals Utilize tools and APIs Make sequential decisions Learn and reflect over time “Without structured context management, agentic AI is just improvisation.” While that sounds promising, there’s a critical oversight … Read more
In the realm of artificial intelligence, current applications often manage “context” in a limited manner, primarily as long strings of conversation logs. While this approach can function effectively, it faces significant limitations: No memory control: AI struggles to retain relevant information across interactions. No role awareness: There’s often a lack of understanding about the roles … Read more
Exploring Innovative AI Tools for Efficiency As technology advances, new AI solutions are transforming how we work and communicate. Here are three key innovations worth noting: Personal AI Workspaces: Imagine a writing assistant that adapts to your unique style, tone, and tasks over weeks, enhancing your productivity. Enterprise QA Copilot: This smart chatbot keeps track … Read more
MCP: Final Wrap-Up — From Prompts to Protocols: The MCP Era Begins In this insightful wrap-up of the Model Context Protocol (MCP), you’ll discover how this innovative approach reshapes the future of AI: Episode 1 emphasized why context is vital for creating smarter and more trustworthy AI solutions. Episode 1.1 linked MCP with agentic AI, highlighting its … Read more
Large Language Models (LLMs) are undeniably impressive, allowing users to engage in fluid conversations by simply prompting them. However, a significant flaw lurks beneath this engaging interface: they forget instantly. Each interaction acts as a complete reset, making consistency and memory challenging—especially for long-term tasks. This poses a problem when you need an AI to … Read more
There’s been a lot of buzz around “agentic AI”, a term that refers to artificial intelligence capable of: Setting and pursuing goals Using tools and APIs Making decisions across various steps Learning and reflecting over time However, the reality behind many flashy demos is quite different. As noted, “Without structured context management, agentic AI is … Read more
Many current AI applications rely on a simplistic approach to “context,” often represented as lengthy logs of conversation. While functional, this method has its limits, leading to issues such as: No memory control: AI lacks the ability to recall past interactions effectively. No role awareness: The AI struggles to understand its position or purpose in … Read more
Exploring Innovative AI Solutions The rise of artificial intelligence is reshaping how we work and communicate. Here are three standout applications: 1. Personal AI Workspaces A writing assistant that not only helps with drafting but also remembers your unique style, tasks, and tone over time. This ensures your writing remains consistent and authentic, saving you … Read more
MCP: Final Wrap-Up — From Prompts to Protocols: The MCP Era Begins You’ve just delved into the Model Context Protocol (MCP) through three insightful episodes, and now it’s time to reflect on the key takeaways: Episode 1 highlighted why MCP is crucial: context enhances AI intelligence and reliability. Episode 1.1 connected MCP to agentic AI, illustrating its … Read more
In the fast-paced realm of technology, a thought-provoking notion has emerged: “apps are dead, prompts are the future.” This idea presents an enticing vision where users interact with AI through simple conversations, relegating traditional apps to the sidelines. However, the reality is that apps continue to hold significant importance, and AI relies on them more … Read more