Essays on the system.
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Remote vs local MCP servers: which one to run
A practical guide to choosing local stdio or remote Streamable HTTP MCP servers, with security and operations tradeoffs, failure modes, and production checklists.
AI for solo founders: the smallest useful setup
A minimal AI stack for solo founders: one host, one coding assistant, four roles, narrow MCP or native tools, and a human approval gate for consequential actions.
Teaching an OS your company facts without training a model
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.
AI for agencies: one OS per client without mixing data
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.
The return is the hour you can point to
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.
Build vs buy an AI agent system: an honest decision framework
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.
Chatbot, copilot, agent, operating system: a buyer's map
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.
Choosing your AI host: the five questions that matter
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.
Cost control for agent systems: where the tokens actually go
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.
Fine-tuning or context: which problem are you solving
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.
How to add an MCP server to Cursor
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.
Measuring return on an AI system without fooling yourself
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.
What to ask an AI vendor before you sign
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.
Orchestration is the operating system, not a bigger chat
What changes when agents share memory and a human gate, and why another chatbot does not close a context gap.
Context gaps show up before the agents do
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.
Fractional chief AI officer: what the role is, and what Agentik sells
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.
An AI implementation plan for a small company, in 30, 60, and 90 days
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.
AI readiness checklist: twelve questions before you spend
An AI readiness checklist for companies about to buy agents. Twelve questions. No score that pretends to be an audit.
AI governance for agents is a human gate
AI governance, here, means a person approves before an agent publishes, sends, or writes to a customer record. This is not a legal opinion.
MCP servers for marketing and SEO: what to judge before you add one
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.
Claude Code for content marketing, without a fake weekly quota
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.
Growth OS: AI agents for marketing teams, no new tool
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.
How to add Agentik’s remote MCP server
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.
Content OS: an AI content agent that stays in the host
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.
One agentic OS, four hosts: Claude, Cursor, ChatGPT, Codex
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.
AI operating system template for Claude: build, or install
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.
What is an AI operating system?
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.
