Tag: Agentic AI
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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.
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The Hidden Advantage of Personal Agents Is Not Automation
The real advantage of personal agents is not chore automation. It is training a system that learns your judgment, uses reliable skills, and becomes a better extension of how you think and work.
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The PocketOS Database Deletion Was Not an AI Failure
The PocketOS incident is not a reason to stop using AI agents. It is a warning that agentic AI needs real governance: least-privilege access, approval gates, audit logs, rollback plans, and guardrails built into the operating model.