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Insights

Insights

Field notes and research from real engagements across the network.

No. 009Beyond RAG: Constructing the Context Enterprise AI Agents Actually NeedContext Engineering · 12 min read · Sep 2026RAG solved how a model reaches information it was not trained on. Enterprise agents face a harder problem: operating inside the right knowledge, application, capability and governance context at runtime. That is a composition problem, not a retrieval one.No. 008AI Coding Is Scaling. Software Intent Is Becoming the Bottleneck.AI-Native Development · 10 min read · Sep 2026AI can increasingly produce the implementation. The scarce resource is shifting to the layer above it — a durable, structured representation of what the software is supposed to be. That is the bottleneck the next phase of AI-native development has to solve.No. 007Personalization vs. Apperception: Selecting an Experience or Composing OneArchitecture · 9 min read · Sep 2026Personalization and apperception can both make software look different for different users. Architecturally they solve opposite problems: personalization selects a predefined experience for a user; apperception constructs the appropriate experience from runtime context.No. 006AI-Native Software Is Not Traditional Software With AI AddedAI-Native Architecture · 11 min read · Sep 2026When software can reason, generate, act and dynamically compose capabilities, you are not adding a feature — you are changing the architecture. This is the shape of the post-AI software era.No. 005Why RAG Is Not Enough for Enterprise AI AgentsContext Engineering · 9 min read · Aug 2026RAG solved the problem of finding information. It did not solve the problem of understanding the software context in which that information matters — and that is the problem production agents actually have.No. 004The End of the Software Application? How AI Agents Will Change SoftwareAI-Native Architecture · 9 min read · Aug 2026The next generation of software may not be a set of large applications that humans navigate. It may be an ecosystem of capabilities that humans and agents dynamically compose.No. 003From Code to Intent: Why AI Will Change the Programming LanguageAI Development · 9 min read · Jul 2026The real limit of AI-assisted development is that humans still express software intent through code, prompts, tickets and fragmented docs. The next programming artefact is intent itself.No. 002The New Software Development Lifecycle: From Requirements to AI AgentsAI Engineering · 10 min read · Jul 2026The real AI revolution in software is not that AI writes more code. It is that AI can participate across the entire lifecycle — from intent to continuous evolution.No. 001Why AI Agents Need an Architecture of Their OwnAgent Architecture · 9 min read · Jun 2026Giving an AI agent access to APIs is not the same as giving it architectural understanding. Enterprise agents fail on permissions, context and boundaries — not on model capability.