writing · tag
AI Agents
16 posts tagged “AI Agents.”
RAG Isn't Magic: When Retrieval Helps and When It Hurts
RAG gets sold as a cure. It's a tool that's excellent for some jobs and actively harmful for others. Here's how to tell which one you've got before you build it.
Read →If You Can Build It in a Weekend, So Can Your Competitor
Wrapping a model isn't a moat. Where AI actually creates defensibility for a startup — and where it just commoditizes you alongside everyone else.
Read →Can You Trust an AI Agent With Your Customer Data?
The data-privacy and security questions every founder should ask before letting an agent touch customers, payments, or PII — in plain language, not a compliance lecture.
Read →40% of AI Projects Get Killed. Here's Why Yours Might.
Gartner says 40%+ of agentic projects are at risk of cancellation. The real reasons agent projects die — unclear success criteria, no data access, eval drift — as a survival checklist.
Read →Job Descriptions for Agents: A Template
The single highest-leverage thing you can do for an AI agent is write it a real job description before you deploy it. Here's the template we use, with every field explained.
Read →What a Manager's Job Becomes When Agents Do the Work
If agents do the producing, what's left for the manager? The job doesn't shrink. It moves up: setting the standard, designing the system, and owning the judgment.
Read →The Agentic OS Maturity Model: 5 Stages
Most teams have no idea how far along they actually are with AI. Here are the five stages from one-off prompting to a real Agentic OS, and how to tell which one you're in.
Read →Why Your AI Demo Won't Survive Real Users
The demo was flawless. Then real users touched it. The gap between a demo that wows and a system that survives is the unglamorous work that decides whether AI ships.
Read →Who's Accountable When the Agent Is Wrong?
Every leader weighing AI eventually hits the real question: when the agent makes a costly mistake, who owns it? If the honest answer is no one, you're not ready to ship.
Read →The Context Layer: Why Your Agent Keeps Getting It Wrong
When an agent gets it wrong, it usually didn't reason badly. It answered correctly from incomplete information. The fix isn't a smarter model. It's a better context layer.
Read →Your AI Doesn't Have a Model Problem. It Has a Data Problem.
You upgraded the model and the output is still wrong. That's the tell. Almost every 'the AI isn't good enough' problem is a data problem wearing a model costume.
Read →When to Fire an Agent (and Hand the Work Back to a Human)
Everyone talks about deploying agents. Almost nobody talks about pulling one. Knowing when to take an agent off a job is a core management skill, not an admission of failure.
Read →Giving an Agent a Performance Review
Evals sound like an engineering chore. They're really the management ritual you already run: a regular, honest look at whether the work is good enough. Here's how to run one for an agent.
Read →Your First AI Agent Is a New Hire. Onboard It Like One.
Your AI agent isn't failing because the model is weak. You skipped its onboarding. Give it a job description, access, context, and a feedback loop like any new hire.
Read →The 70/30 Method: Building With AI Agents Without Betting the Company on Them
Why I let AI agents handle about 70% of the work and keep 30% for senior judgment — and how that ratio keeps AI projects out of the ditch.
Read →How I Build AI Agents That Actually Ship
Most AI agents die in the demo. Here's the process I use to get them into production — and the unglamorous parts that decide whether they survive contact with real users.
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