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Enterprise AI, explained for builders and buyers. Page 9 of 29

Practical writing on governed agent orchestration, on-premise AI, compliance, and the infrastructure decisions that separate pilot projects from production platforms.

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261 Articles
39 Topics
12 Featured
Blue and black cables plugged into hardware ports, standing in for the MCP servers that connect AI agents to enterprise systems
AI Security 12 min read

Best MCP Servers for Enterprise Teams in 2026: A Vetted List and an Approval Checklist

Official MCP servers from GitHub, Microsoft, Atlassian, Slack, Google, AWS and Salesforce, with the transport and authentication model of each, and the checklist a security team should run before any agent is allowed to call one. Checked against maintainer documentation in September 2026.

#MCP servers#Model Context Protocol#MCP security
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Padlock resting on a computer keyboard, representing the security and provenance review that comes before self-hosting DeepSeek open weights on premises
AI Security 11 min read

DeepSeek On-Premise: A Provenance and Risk Review Before You Self-Host V4

Self-hosting DeepSeek weights is a different risk decision from using DeepSeek's app or API. This review covers the September 2026 lineup, what public-sector restrictions actually cover, a provenance checklist for the weights, and the GPU arithmetic.

#DeepSeek#open-weight models#local LLM
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Close-up of a memory module with DRAM chips and gold edge contacts, a reminder that memory capacity and bandwidth decide which local models a desk-side AI system can run
AI Infrastructure 11 min read

DGX Spark vs Mac Studio vs Ryzen AI Max+ 395: Local LLM Hardware Compared (2026)

NVIDIA DGX Spark, Apple's M5 Max and M5 Ultra Mac Studio, and AMD's Ryzen AI Max+ 395 systems compared from their spec sheets: memory, bandwidth, stated compute, software stack, clustering, power and price, and where each fits in an enterprise AI plan.

#local AI hardware#DGX Spark#Mac Studio
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Developer in a dimly lit room reading code on two monitors, representing hands-on evaluation of open-source AI agent harnesses
AI Platforms 10 min read

Hermes Agent vs OpenClaw: Architecture, Skills, Security and Fit

A neutral comparison of the two most popular open-source personal agent harnesses: how OpenClaw's gateway and Hermes Agent's learning loop differ in architecture, skills, memory, channels, local models, sandboxing, security record and stewardship, and which suits which job.

#Hermes Agent#OpenClaw#agent harness
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Rack of enterprise server equipment in a dark data hall, representing the multi-node GPU capacity a 2.8-trillion-parameter Kimi K3 deployment requires
AI Infrastructure 8 min read

Kimi K3 On-Premises: What Serving a 2.8-Trillion-Parameter Open Model Really Takes

Moonshot AI published the full Kimi K3 weights in July 2026. Self-hosting them is allowed, but the checkpoint alone is about 1.56 TB. Here is the license in plain terms, the memory arithmetic per node, what multi-node serving involves, and when a smaller model is the better call.

#Kimi K3#open-weight models#mixture of experts
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Combination padlock resting on a white computer keyboard, representing the access controls an enterprise needs around a self-hosted OpenClaw gateway
AI Security 11 min read

OpenClaw Security for Enterprises: Risks, CVEs and Controls

What OpenClaw's own security model assumes, which advisories and skill-registry incidents are on the public record, how to harden a gateway using the official documentation, and when an enterprise should move a workload to a governed platform instead.

#OpenClaw#AI security#AI agent governance
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An engineer adjusting components inside an illuminated server rack, representing the GPU tiers that decide which Qwen model an enterprise can run on premises
AI Infrastructure 9 min read

Qwen On-Premise: Choosing a Qwen3.5, 3.6 or 3.8 Model by Size Tier

The Qwen family now runs from sub-1B edge models to a 2.4-trillion-parameter flagship, with different licenses at the top end. A tier-by-tier guide to dense versus MoE, coder and vision variants, official FP8 and Int4 builds, long context, tool calling and the GPUs each tier needs.

#Qwen#open-weight models#local LLM
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Inside a desktop PC: a motherboard with an M.2 drive installed beneath a lit Radeon graphics card, representing workstation hardware for running a local LLM
AI Infrastructure 9 min read

How to Run OpenClaw and Hermes Agent on a Local LLM

A practical setup guide for pointing OpenClaw or Hermes Agent at a model on your own hardware: choosing between Ollama, vLLM and llama.cpp, the documented configuration for each harness, Docker isolation, MCP servers, and how to size a tool-calling model to your GPU memory.

#OpenClaw#Hermes Agent#local AI infrastructure
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Three colleagues reviewing work on laptops in an open-plan office, representing relationship managers preparing client briefings with a private AI assistant that respects bank information barriers
Finance AI 6 min read

AI for Relationship Managers: Client Briefings Without Breaching Information Barriers

A relationship manager assistant that pulls client context from every system is an obvious win and an obvious risk. In a bank with private-side functions, the naive design turns the retrieval index into a hole in the information barrier. How to build the assistant so the wall survives it.

#financial services AI#private AI#on-premises AI
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