Context Engineering Explained: The Secret to Reliable AI Agents

Are your AI agents breaking in production despite using perfect prompts? You’re not alone. In this video, Developer Relations Engineer Quinton Wall breaks down Andrej Karpathy's viral insight on why "Prompt Engineering" is the wrong mental model for building industrial-strength LLM applications. Discover why AI performance isn't about wordplay, but rather about "Context Engineering" and filling the context window with the right information at the exact right time. Key Takeaways: • Prompt vs. Context: Understanding everything the model actually sees (system messages, conversation history, tool results, retrieved memory) • The Real Reason AI Agents Fail: Context window noise, stale tool calls, and bloated system prompts • Real-World Example: Lessons learned from building AXRank (evaluating LLM agent API usability) • The 4 Pillars of Context Engineering: External memory storage, selective inclusion, token compression, and dynamic RAG retrieval Resources & Links: • Explore Postman AI Tools: https://postman.com #AI #PromptEngineering #ContextEngineering #LLM #ArtificialIntelligence #SoftwareEngineering #DeveloperTools #Postman #AIAgents