Over the last few years, I’ve seen the same pattern again and again. The agile movement helped me a lot. It pushed me to focus on real outcomes for users, and on changes that are clear and valuable for buyers. Product management brings the whole picture and structures the discovery phase. From a product marketing point of view, it became easier to speak about pains and benefits in simple terms. But with executives, something was still missing. At first, I spoke about process. Then I tried to speak about “value”. Every time it was too long, too fuzzy, and it didn’t really land. I did not have a short, clear way to explain why a product decision mattered in a language that made sense for executives. That’s why Rich Mironov’s talk “ Crafting business cases that win ” at Productized conference really connected with me. He starts from a very clear point: most executives do not care about our backlogs, frameworks, or internal product practices. They care about revenue this quart...
I'm currently learning about AI agents and thought it would be helpful to share my findings. Below, I've grouped AI agent technologies into 13 main categories. Each category has a brief explanation to make things clearer. After the categories, you'll find a list of the technologies with links to their websites for further reading. In progress, will be updated. 1. Design & Architecture Frameworks These are the foundational patterns and models that define how AI agents are structured and behave. They include various architectural approaches like reactive and proactive designs, cognitive models like BDI (Belief-Desire-Intention), and frameworks that specify how agents interact with their environments. The newer ReAct Pattern and chain-of-thought training frameworks help agents reason and act more effectively. 2. Development & Building Tools These tools facilitate the creation of AI agents, from comprehensive frameworks like LangChain and Microsoft AutoGen to vi...