Essays on the system.
Definitions, install, and prices.
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Company facts change too often to hide inside model weights. Use memory, retrieval, access control, and approval gates to teach an agentic OS what your company knows without retraining a model.
How marketing agencies can run one AI OS per client without treating workspaces as security isolation. Covers retrieval boundaries, MCP authorization, scoped credentials, approval gates, and failure modes.
How to talk about return on an AI operating system without borrowing someone else's percentage. Time saved, mistakes avoided, revenue the company already measures.
Eight questions for an AI vendor, with what a good answer sounds like, what an evasive answer sounds like, and the follow-up that ends the dodge.
How to measure AI ROI: a real baseline, events you log per task, outcome metrics rather than output counts, and a fair before and after when volume moves.
Where Cursor keeps mcp.json, what stdio and remote HTTP entries look like, how to check the server is really connected, and what each failure means.
Fine-tuning vs RAG is a diagnosis, not a preference. Fine-tuning moves behaviour and format. Retrieval moves facts. Here is how to tell which one you need.
A profile of the six places an agent system spends tokens, how to measure each one, and the fixes in order of payoff, with the vendor numbers cited.
Five operational questions for picking the app that runs your model when you plan to install tools over MCP, each with a ten-minute test you can run.
Four categories, placed on six things a buyer can check: what you install, who runs the model, what it remembers, what it may do alone, what it costs, how it fails.
Build vs buy for AI agents is a question about who owns auth, memory, evals, retries and the pager. Here is the cost of each side, with the assumptions written down.
AI governance, here, means a person approves before an agent publishes, sends, or writes to a customer record. This is not a legal opinion.
An AI readiness checklist for companies about to buy agents. Twelve questions. No score that pretends to be an audit.
An AI implementation plan template for a small business: name the host, install one OS, then decide if on demand is required. No invented ROI.
A fractional chief AI officer is a part-time CAIO. Agentik on demand is priced on the gap we prove, not on a published 3,000 to 60,000 euro menu.
Companies that are not AI-native fail in the missing context, not in the model. A gap analysis names what the operating system still cannot see.
What changes when agents share memory and a human gate, and why another chatbot does not close a context gap.
A useful MCP server for marketing states auth, read or write, and who pays. Content OS and Growth OS sit above the connectors. This is not a paid ranking.
Add the remote MCP server at mcp.agentik-os.com/api/mcp in ChatGPT, Claude, Cursor, or Codex. The marketing site does not serve the call.
AI agents for marketing teams run inside Growth OS on the host you pay. The OS looks for the gap. It does not replace the person who approves the send.
Claude Code for content marketing works when the workflow is named. This is the workflow. It does not claim a piece count we have not measured.
Content OS is the AI content agent system for creation, SEO, and the next cut of a piece. It runs in the host you pay. It is not a CMS.
The same Agentik OS installs on Claude, Cursor, ChatGPT, and Codex. Hermes is a fifth host. MCP is the control plane. The site is not the call.
An AI operating system template for Claude is a starter repo. Installing an OS over MCP is the other path. Here is the honest difference.
An AI operating system is specialized agents, memory, and skills on the host you already pay. It is not a chatbot, and it is not software you install beside your tools.