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The world is leaping towards building products with agentic architecture; those powered by autonomous, goal-driven AI agents. These systems won’t just respond; they will reason, plan, and act independently. But with such autonomy comes complexity. Traditional engineering and product management models are no longer sufficient. Enter Context-Driven AI Engineering,  an approach that embeds the surrounding situational, user, and system-level context into every stage of AI product development. For product managers (PMs) working at the intersection of data, design, and delivery, this isn't just a methodology, it's a necessity. Why Context is Critical in AI Engineering Autonomous agents interact with complex environments: users, APIs, documents, dynamic tasks, and even other agents. Without context-awareness, they risk becoming brittle, hallucinating, or acting inappropriately. Context isn't just data, it's relevance . It includes: User context : Who is interacting, with wha...

(image generated by Meta AI 😐) Feeling overwhelmed with AI already, while the world has just started to feel the importance of AI ethics! Trust me, you are not alone. Most AI professionals struggle with ethical frameworks. But there's hope. Here are 5 underrated frameworks that will transform your approach: 1. The Algorithmic Impact Assessment (AIA) Often overlooked, AIAs are powerful tools for evaluating AI systems before deployment. They force us to consider potential societal impacts, not just technical performance. Why it is revolutionary: AIAs help identify and mitigate unintended consequences, especially for marginalized groups. 2. The Ethics Canvas Inspired by the Business Model Canvas, this visual tool helps teams map out ethical considerations systematically. Why it matters: It turns abstract ethical discussions into concrete action items, making ethics a practical part of development. 3. The Ethical OS Toolkit Developed by the Institute for the Future, this framework hel...