8 articles
Quality is the top-cited reason AI agent pilots stall, yet barely half of teams run evaluations at all. What an AI agent evaluation framework actually has to check — trajectory, not just output — and how to build one before you scale.
Generic vendor checklists ask about security and pricing, which every vendor passes. Here are the pass/fail questions a demo can't fake, and the specific risk each one exposes.
Most SaaS products need one or two AI features done well, not nine done shallowly. A filter for telling which pitched features are load-bearing and which are decoration nobody uses.
The 838%-ROI claims in vendor pitches are built on assumptions that don't survive scrutiny. A grounded formula for AI automation ROI, and the costs that get left out.
Skip the feature-comparison table. Four questions that tell you whether your workflow needs a chatbot or a true agent, plus how to expose agent-washing in a vendor demo.
Most AI agent pilots stall before production. The causes are architectural, not prompt quality: unbounded autonomy, lost state, no evaluation and no rollback. Here is what to build instead.
A build vs buy framework for AI features — where buying wins, where a thin wrapper is genuinely correct, and the three conditions that justify building AI infrastructure yourself.
Where the money goes when you build an AI product MVP — engineering time, model inference, data preparation, evaluation — and which scope decisions move the total most.