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In today's episode, we dive deep into Docker's bombshell analysis revealing why so many AI agent deployments are crashing and burning in production environments. Spoiler alert: it's not the AI that's the problem—it's how we're implementing the Model Context Protocol. We break down the three critical misconceptions that are sabotaging enterprise AI initiatives: Why treating MCP like a REST API is setting you up for failure The crucial difference between tools and agents (and why confusing them kills reliability) How ignoring MCP's full component suite leaves massive gaps in your implementation This isn't just another "AI is hard" story. Docker's insights reveal a fundamental architectural principle that separates successful AI deployments from expensive mistakes: the clean separation between non-deterministic AI reasoning and deterministic system execution. Whether you're a developer building AI tools, an enterprise IT leader evaluating AI agent platforms, or a product team designing the next generation of intelligent automation, this episode gives you the framework to avoid the pitfalls that are tripping up even experienced teams. Key Takeaways: MCP's true role as an architectural seam in AI systems Why comprehensive context management beats rapid prototyping How proper MCP implementation enables observability and governance The shift toward reliability-first AI agent frameworks Subscribe for daily automation insights Read the full article here: https://verulean.com/news/2025-09-03/ This episode is essential listening for anyone serious about deploying AI agents that actually work in the real world.