Tag: Agentic AI
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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.
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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.
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AgentBaiting: The AI Supply-Chain Risk Hiding in Skills and MCP Servers
Attackers are now designing fake Skills and MCP servers for AI agents to find and recommend. Enterprise AI governance must include a controlled software supply chain for agent capabilities.
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AI Regression Testing: A New Model Can Improve Your Benchmark and Break Your Workflow
A model upgrade can improve benchmarks while changing how an AI workflow behaves. Enterprise teams need behavioral regression testing before they swap the brain of a critical process.
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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.
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Prompts vs. Loops: Why Enterprise AI Needs Better Definitions of Done
Prompts still matter, but they are no longer the primary unit of work. Enterprise AI needs loops: outcome-driven systems with success criteria, verification, retry logic, escalation, and clear definitions of done.
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The Most Dangerous AI Hallucination Is the One That Looks Approved
AI hallucinations are not just model failures. They become enterprise risk when false claims survive the workflow, pass review, and turn into professionally formatted liabilities.
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The Hidden Cost of AI Democratization Is Agent Sprawl
AI democratization is powerful, but without visibility it creates agent sprawl: duplicate tools, unclear ownership, inconsistent outputs, and hidden governance risk.
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MCP Is for the AI. MCP Apps Are for the User.
MCP lets AI connect to tools and workflows. MCP apps create the human interaction layer for verification, review, approval, and trust.
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The Next AI Skill Is Knowing How to Define Done
Tools like Codex and Claude Code are moving beyond one-shot prompts. /goal points to a better delegation model: clear outcomes, success criteria, constraints, evidence, and human checkpoints.