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The Reasoning Trap: Why Chain-of-Thought Prompting Doesn't Always Work Large language models have become remarkably adept at showing their work. Give a model a math problem and it will walk you throug...
Why AI Agents Are Failing at Memory — and What Actually Works Every AI agent demo looks impressive. Until you try to use it for more than a day. Ask a coding agent to review your PR on Monday. It nail...
As multi-agent AI systems move from prototype to production, memory — not reasoning — is emerging as the true bottleneck. This article explores three proven memory patterns, the unsolved problem of semantic drift, and what a principled multi-agent memory architecture actually requires.
MCP (Model Context Protocol) is Anthropic's open standard that gives AI agents a consistent way to connect to the tools and data sources they need — think USB for AI agents. This article explores what MCP is, why it matters, the current state of the ecosystem, and the gaps that still need filling.
Multi-agent AI systems are shifting software development from single-model tools to orchestrated agent teams. This article explores what that means for engineers, the new bottlenecks of context engineering, and why the real value now sits in the integration layer rather than the underlying model.
The battle for the largest AI context window is heating up, with MiniMax, Anthropic, and Google all pushing the boundaries of what models can hold in memory at once. But context window size is only half the story. The other half — attention quality at range — may determine which applications actually win.
Agentic AI — systems that perceive, plan, act, and adapt — is not an incremental upgrade to large language models. It is a fundamentally new paradigm for AI application design. This article explores the architecture, real-world applications, challenges, and what is coming next.
AI coding tools are extraordinarily good at producing code that solves today's problem. They're poor at producing code that anticipates future problems, that documents its own reasoning, that leaves the codebase in a state where the next engineer can work on it effectively. This is the hidden cost nobody's measuring.
Context windows are the new RAM. As LLMs support 128K, 256K, even 1M tokens, the bottleneck has shifted from how much you can put in context to how efficiently you manage it. This article explores the emerging discipline of context engineering — hierarchical summarization, structured context frames, and semantic chunking — and what it means for the next decade of AI-native software development.