<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Momentaj Insights</title><description>Research notes and working patterns from building AI agents, MCP servers, retrieval systems and AI-native products.</description><link>https://momentaj.com/</link><language>en-ca</language><atom:link href="https://momentaj.com/rss.xml" rel="self" type="application/rss+xml"/><item><title>Your website has a new visitor: AI agents</title><link>https://momentaj.com/insights/your-website-has-a-new-visitor/</link><guid isPermaLink="true">https://momentaj.com/insights/your-website-has-a-new-visitor/</guid><description>An MCP server for a website lets AI agents search the site, ask it questions and act on it for a person, and who runs that server decides what agents see. A summary of founder Amir Pournasserian&apos;s October 2026 survey of 44 tools, and what it means for a business.</description><pubDate>Thu, 08 Oct 2026 16:00:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>MCP</category><category>AI agents</category><category>Websites</category></item><item><title>Should your product have an MCP server? A decision guide</title><link>https://momentaj.com/insights/should-your-product-have-an-mcp-server/</link><guid isPermaLink="true">https://momentaj.com/insights/should-your-product-have-an-mcp-server/</guid><description>An MCP server lets AI agents in ChatGPT, Claude and other hosts use your product through a small set of tools. This decision guide for SaaS and platform teams gives five signals to build one now, three to wait, and what a good first server looks like.</description><pubDate>Thu, 08 Oct 2026 15:50:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>MCP</category><category>AI strategy</category></item><item><title>Keeping AI agents honest: the server-side grounding pattern</title><link>https://momentaj.com/insights/keeping-ai-agents-honest-server-side-grounding/</link><guid isPermaLink="true">https://momentaj.com/insights/keeping-ai-agents-honest-server-side-grounding/</guid><description>Server-side grounding is a pattern in which an AI agent returns only identifiers and the server builds everything the user sees from real tool results, so the agent cannot show an invented item. How we built it for MayAI, the six rules around it, and where it applies.</description><pubDate>Thu, 08 Oct 2026 15:40:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>AI agents</category><category>Case study notes</category><category>.NET</category></item><item><title>What each AI host requires of an MCP server</title><link>https://momentaj.com/insights/what-each-ai-host-requires-of-an-mcp-server/</link><guid isPermaLink="true">https://momentaj.com/insights/what-each-ai-host-requires-of-an-mcp-server/</guid><description>Each AI host adds its own rules to the MCP specification, so a compliant server can still be rejected by the Claude directory or the ChatGPT plugin directory (shared with Codex). The rules of eight hosts as of 1 October 2026, where they conflict, and how we build to them.</description><pubDate>Thu, 08 Oct 2026 15:30:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>MCP</category></item><item><title>Testing an MCP server: what exists and what is still missing</title><link>https://momentaj.com/insights/testing-an-mcp-server-what-exists-and-what-is-missing/</link><guid isPermaLink="true">https://momentaj.com/insights/testing-an-mcp-server-what-exists-and-what-is-missing/</guid><description>Testing an MCP server covers six jobs: inspection, evaluations of tool choice, design audits, host-rule checks, security scans and load tests. Founder Amir Pournasserian reviewed about eighty tools and compared thirty-one on 1 October 2026: every capability exists somewhere, but no open, local tool combines them.</description><pubDate>Thu, 08 Oct 2026 15:20:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>MCP</category></item><item><title>RAG for code and other structured material: why token-count chunking fails</title><link>https://momentaj.com/insights/rag-for-code-and-structured-material/</link><guid isPermaLink="true">https://momentaj.com/insights/rag-for-code-and-structured-material/</guid><description>Most retrieval-augmented generation (RAG) systems split documents by token count, which cuts code, contracts and catalogues into fragments that look right and miss the answer. What we built for DevGuardian AI instead, from code-aware chunks to a dependency graph and incremental refresh, and how it applies beyond code.</description><pubDate>Thu, 08 Oct 2026 15:10:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>RAG</category><category>Case study notes</category></item><item><title>Multi-agent systems in .NET: what we learned building two of them</title><link>https://momentaj.com/insights/multi-agent-systems-in-dotnet-what-we-learned/</link><guid isPermaLink="true">https://momentaj.com/insights/multi-agent-systems-in-dotnet-what-we-learned/</guid><description>A multi-agent system is one in which several AI agents, each with its own role and tools, work on the same task. Seven lessons from building two agent systems on Microsoft Agent Framework in .NET: DevGuardian AI, with three review agents, and MayAI, with one agent and six tools.</description><pubDate>Thu, 08 Oct 2026 15:00:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>.NET</category><category>AI agents</category><category>Case study notes</category></item><item><title>From public data to market intelligence: how Valco AI was built</title><link>https://momentaj.com/insights/from-public-data-to-market-intelligence/</link><guid isPermaLink="true">https://momentaj.com/insights/from-public-data-to-market-intelligence/</guid><description>Valco AI is the platform we built for Valco Properties to turn Dubai&apos;s daily public property records into market intelligence and a conversational assistant for its staff. The pipeline, the data model, the forecasting models and the conversational layer, and the order we built them in.</description><pubDate>Thu, 08 Oct 2026 14:50:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>Machine learning</category><category>Case study notes</category><category>RAG</category></item><item><title>AI readiness for Canadian businesses: a checklist of what has to be true first</title><link>https://momentaj.com/insights/ai-readiness-checklist-for-canadian-businesses/</link><guid isPermaLink="true">https://momentaj.com/insights/ai-readiness-checklist-for-canadian-businesses/</guid><description>An AI readiness assessment checks whether the workflow, data, privacy rules, guardrails, platform and people around a process are ready before anyone builds AI. These are the questions we ask in ours, written as a checklist you can run yourself, with a way to score the answers.</description><pubDate>Thu, 08 Oct 2026 14:40:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>AI strategy</category></item><item><title>Why a CMS should let AI operate and people govern</title><link>https://momentaj.com/insights/ai-first-cms-ai-operates-people-govern/</link><guid isPermaLink="true">https://momentaj.com/insights/ai-first-cms-ai-operates-people-govern/</guid><description>An AI-first CMS is a content management system built for an AI agent to operate, with people deciding what it may publish. Founder Amir Pournasserian&apos;s October 2026 design: tools over MCP, rules that become checks, facts stored once and a review desk, and why we plan to prototype it in FluentCMS.</description><pubDate>Thu, 08 Oct 2026 14:30:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>CMS</category><category>AI agents</category><category>Open source</category></item><item><title>Documentation you can audit: observed, documented, inferred</title><link>https://momentaj.com/insights/documentation-you-can-audit-observed-documented-inferred/</link><guid isPermaLink="true">https://momentaj.com/insights/documentation-you-can-audit-observed-documented-inferred/</guid><description>Auditable documentation labels every statement with where it came from: observed in the code, documented by a person or inferred by a model. Founder Amir Pournasserian compared seventeen AI documentation tools on 1 October 2026, found that none shows this, and designed a fact base that does.</description><pubDate>Thu, 08 Oct 2026 14:20:00 GMT</pubDate><dc:creator>Amir Pournasserian</dc:creator><category>Documentation</category><category>RAG</category></item></channel></rss>