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🤖🥀➡️❓🚀 Prompt Engineering Is Dead. Context Engineering Is Dying. What Comes Next Changes Everything.

🤖 AI Summary

• 📉 Klarna initially saved 60 million dollars using an AI agent that did the work of 853 employees but sacrificed customer relationships and brand reputation in the process [00:00].
• 🎯 The core failure in enterprise AI today is the intent gap where models optimize for measurable metrics like speed instead of actual organizational goals like customer lifetime value [02:11].
• 🏗️ Prompt engineering was individual and session based while context engineering focuses on the information state an AI operates within using RAG pipelines and MCP servers [05:08].
• 🧠 Intent engineering is the emerging discipline of encoding organizational purpose, values, and decision boundaries into machine readable and actionable parameters [06:20].
• 📊 Recent Deloitte and KPMG reports show massive AI investment but 74 percent of companies globally report no tangible value yet because they haven’t redesigned jobs or governance around AI [07:14].
• 🛑 Microsoft Copilot adoption has stalled at scale because deploying tools without organizational intent alignment creates activity without measurable impact [09:40].
• 🔌 The Model Context Protocol (MCP) is the new standard for connecting agents to data but requires architectural and political decisions about access and governance [12:17].
• 🧱 Organizations must move from human readable aspirations like increase customer satisfaction to agent actionable objectives with specific resolution hierarchies [18:12].
• ⚖️ Senior human employees carry institutional knowledge and judgment that agents cannot absorb through osmosis; this knowledge must be made explicit for AI to function [19:14].
• 🏁 The AI race has shifted from an intelligence race between models to an intent race where the winner is the organization that builds the best alignment infrastructure [26:04].

🤔 Evaluation

• ⚖️ While the video emphasizes intent engineering as the primary bottleneck, the report Artificial Intelligence and the Future of Teaching and Learning by the U.S. Department of Education highlights that human in the loop remains the essential safety net for ethical alignment.
• 🔍 Research from the Harvard Business Review in the article Reskilling for the AI Era suggests that the organizational struggle is less about technical intent and more about a fundamental lack of data literacy among leadership.
• 🌐 To gain a deeper understanding, one should explore the technical specifications of the Model Context Protocol (MCP) and academic papers on Multi Agent Systems (MAS) to see how coordination is handled mathematically.

❓ Frequently Asked Questions (FAQ)

🤖 Q: What is intent engineering in the context of AI agents?

🤖 A: It is the practice of converting human values and business trade-offs into structured, machine-readable parameters that guide how autonomous agents make decisions without constant human supervision.

📉 Q: Why did Klarna have to rehire human agents after their AI success?

📉 A: The AI optimized for resolution speed so effectively that it became robotic and dismissive, damaging long-term customer relationships that required the nuanced judgment only experienced humans possessed.

🔌 Q: How does the Model Context Protocol (MCP) help businesses?

🔌 A: MCP provides a standardized way for AI agents to securely connect to various data sources like Slack, Salesforce, and Google Docs, preventing the creation of disconnected shadow agents.

🏢 Q: Why is Microsoft Copilot seeing low adoption rates in large enterprises?

🏢 A: Usage often stalls because the tool is deployed as a generic software add-on without integrating it into specific organizational workflows or defining clear intent for what the AI should accomplish.

📚 Book Recommendations

↔️ Similar

• 🤖🧑‍ Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell explores the challenge of aligning superintelligent machines with human objectives to ensure they remain beneficial.
• ⚖️🤖 The Alignment Problem by Brian Christian examines the history of machine learning biases and the technical hurdles in making AI understand human values.

🆚 Contrasting

• 📘 The Myth of Artificial Intelligence by Erik J. Larson argues that current AI lacks the capacity for true intuition and abductive reasoning, suggesting intent engineering may never fully replace human judgment.
• 📘 Rebooting AI by Gary Marcus and Ernest Davis proposes that we need a bottom-up approach to AI based on cognitive science rather than just better goal-setting for current statistical models.

🎨 Creatively Related

• 📘 Team of Teams by General Stanley McChrystal describes how to build organizational structures that can adapt to rapid change, mirroring the need for flexible AI agent frameworks.
• 📘 High Output Management by Andrew Grove outlines the original philosophy behind OKRs which served as the human precursor to modern intent engineering.