Tag: AI Agents
-
Internal AI Is Not Harmless: Treat It Like Production Infrastructure
Internal AI often gets less scrutiny than customer-facing systems while gaining access to data, APIs, cloud services, and tools. That makes it a high-privilege control plane, not a harmless chatbot.
-
Agent-First Design: Enterprise Software Has a Second User
As AI agents begin doing work on behalf of employees, enterprise software will be evaluated not only for people, but for agents that need governed access to data, actions, and workflows.
-
Sharing AI Assets: The New Spreadsheet Problem
AI makes it easy for employees to build skills, MCPs, agents, and workflows. Without a shared capability layer, enterprises recreate the spreadsheet era at agent speed.
-
AI Security: When the Companies Creating the Risk Sell the Defense
AI agents create a new class of enterprise risk. The answer is not blind trust or cynical paralysis—it is defined access, ownership, controls, and a real way to stop them.
-
Graph Engineering: Giving AI an Org Chart
Graph engineering designs how specialized AI agents, tools, systems, and humans coordinate to produce a business outcome—without recreating organizational chaos at machine speed.
-
Generative AI, Agentic AI, and AI Agents: The Boardroom Distinction That Matters
Generative AI creates content. Agentic AI pursues goals. AI agents are the workers that combine models, tools, rules, and workflow context to execute an approved job.
-
OpenAI’s Enterprise Agents Do Not Eliminate AI Services. They Prove Why They Matter.
Enterprise AI platforms do not make implementation optional. They increase the need for workflow redesign, trusted data, governance, adoption, and accountable execution.
-
Your AI Prototype Is Only a Prototype Until It Has Credentials
JADEPUFFER is not just a ransomware story. It is a warning that public-facing AI prototypes with credentials quickly become enterprise attack surface.
-
Your AI Agent Can Read Every Document and Still Miss the Point
Enterprise AI does not only need more documents or bigger context windows. Knowledge graphs and Graph RAG help AI understand the relationships between customers, products, tickets, contracts, and business risk.
-
Your AI System Has a Middle-Management Problem
Multi-agent AI systems do not fail only because the agents are not smart enough. They fail because nobody designed the management layer: routing, squads, tools, workflow graphs, and parallel execution.