Analysis takes overwhelming, unstructured information and transforms it into clear, actionable insights. It’s not just about crunching numbers—it’s about creating clarity from chaos. A good analytical agent doesn’t just spit out charts. It identifies trends, anomalies, and meaningful patterns you might otherwise overlook. It segments data neatly, benchmarks performance clearly, and contextualizes everything to make your next steps crystal clear. Think of AI in cybersecurity. Analytical agents sift through petabytes of network traffic logs, system events, and threat intelligence feeds in real-time to identify anomalous patterns indicative of a cyberattack. For example, Darktrace uses AI that learns the unique "pattern of life" for an organization's network and then flags deviations that could signify a breach (see Darktrace AI Cyber Security Solutions). In healthcare, AI analyzes medical images (X-rays, MRIs) to detect early signs of…

Generation is likely the part of generative AI you’re most familiar with—it's right there in the name. But this isn’t just about making things. It’s about making things that matter. It’s the AI equivalent of your most creative colleague—the one who somehow always has the perfect idea ready. Need marketing copy? It spins out engaging, targeted messaging. Need visuals? It creates original, compelling imagery. Need code, a strategic plan, or a witty social post? Generation steps in and delivers. Generation is powerful because it adapts. It can produce not just one idea, but multiple tailored versions, each adjusted to suit different audiences, contexts, and goals. It’s creativity at scale, without sacrificing relevance or specificity. Beyond marketing copy, consider AI code generation tools like GitHub Copilot (see GitHub Docs - Getting started with a Copilot…

Tool Calling lets your agentic AI tap into specialized external resources, APIs, and services. Think of it less as giving AI a cape, and more like handing it a Swiss Army knife—suddenly, it's got the right tool for any job. Your AI doesn’t have to master everything itself—it just needs to know where to find the best solution. That’s tool calling in a nutshell. It delegates complex tasks to better-suited tools, integrates with analytics platforms, handles device-level interactions, and automates tedious steps. Instead of reinventing the wheel, your AI borrows one that spins better. This is becoming incredibly prevalent. Many large language models now have built-in capabilities for tool calling (also known as function calling or plugin integration). For example, a travel planning AI agent might call a flight booking API…

Reflection allows an AI to pause, look back at what it has done, evaluate the outcomes, and refine its approach based on those insights. If autonomy enables action without constant oversight, reflection ensures those actions continuously improve. When an agent reflects, it begins with a performance review of its recent decisions and actions. It carefully analyzes outcomes, identifying which steps achieved their intended results and which fell short. Instead of blindly repeating the same actions, it can detect patterns—recognizing recurring mistakes and successful strategies alike. Through reflection, the agent gains insight into its own strengths and weaknesses. This self-awareness lets it leverage what it does best while actively working to improve areas of difficulty. If a certain approach repeatedly proves ineffective, the reflective agent won't just recognize it—it actively experiments with…

Autonomy is where things start to feel different. It’s also where things start to feel real. Because autonomy is what elevates AI from being reactive — waiting for instructions, following your every click — into something that can operate on its own, on your behalf, within reason. In agentic systems, autonomy means the ability to take initiative. The agent decides what to do next. It understands goals, prioritizes actions, allocates resources, and handles exceptions — all without waiting for a nudge. That doesn’t mean it’s unbounded. It operates within defined limits, like organizational rules, user preferences, or legal guardrails. But within those lines, it has freedom to move. This isn’t about removing humans. It’s about removing bottlenecks. Think of autonomous systems in supply chain management. An AI agent could monitor global…

🧠 M – Memory: The Context That Makes AI Human-like Memory isn't just remembering the last message in a chat—it’s about continuity, personalization, and the ability to evolve. Short-term memory (aka context window) is what lets AI keep up with you in real time. Models like Gemini have taken this far, holding millions of tokens at once. It’s like having a photographic memory for the current moment. Helpful? Yes. Sufficient? Not even close. Long-term memory is where the magic of agentic AI begins. It allows an agent to remember you, your goals, your tone, your history—and to adapt accordingly. It can draw on past conversations, stored knowledge, and personal preferences. That’s what makes it more than a chatbot. That’s what makes it a partner. Effective long-term memory involves more than just…

The ongoing evolution of Artificial Intelligence presents both significant opportunities and new complexities for the business world. We've moved beyond AI systems limited to narrow tasks and are now encountering the more sophisticated potential of Agentic AI – systems designed to understand broader goals, operate with a degree of independence, learn from interactions, and execute tasks on our behalf. As these advanced AI capabilities become more prevalent, the challenge for curious businesspeople is to look past the often-inflated rhetoric and gain a clear, practical understanding of what these systems can genuinely offer. How can we effectively evaluate their potential, differentiate true innovation from transient trends, and ultimately determine how Agentic AI can bring tangible value to our operations and strategies? To address this need for clarity, my book, Generative AI for…

Synthesis is more than summarization. It’s the AI equivalent of a detective assembling clues across scattered scenes. It means pulling threads from wildly different sources, weaving them into something coherent, insightful, and even novel. A truly synthetic AI doesn’t just know stuff — it knows how to combine things in ways that matter. This capability enables an agent to integrate information across domains, formats, and timelines — something humans often struggle with at scale. It connects concepts that might appear unrelated on the surface, linking technical documentation with market trends or financial reports with behavioral cues. From these fragmented inputs, synthesis allows agents to build coherent narratives that help people make sense of complex realities. It also gives rise to useful explanations — not just lists of facts, but interpretations. Synthesizing…