writing · tag
AI Agents
20 posts tagged “AI Agents.”
Your Agent Is Only as Safe as the Tools You Let It Call
The model isn't your attack surface. The pile of MCP servers and integrations you wired up in an afternoon is. Agent security is a permissions problem before it's a prompt problem.
Read →The Handoff Is the Product
Everyone designs the happy path and bolts escalation on later. But what your agent does when it doesn't know is the entire customer experience.
Read →Stop Asking "Can AI Do This?" Ask "What Happens When It's Wrong 5% of the Time?"
Every automation decision is really an error-budget decision. Some workflows survive a 5% failure rate and some die at 0.5%. Founders keep evaluating capability when they should be pricing failure.
Read →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 →Should You Pay Your AI by the Job, Not the Seat?
Outcome-based pricing is replacing per-seat SaaS for AI agents. When paying per completed job is a great deal for a founder — and when it quietly costs you more.
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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