Practical insights on building custom AI agents, agentic workflows and automation - for businesses and professionals who want to put AI to work.
A practical guide to introducing AI agents at work through bounded workflows, clear ownership, hands-on learning, feedback and useful outcome metrics.
How to measure AI agent performance before scaling: a practical scorecard for task success, quality, risk, cost, escalation and human review.
A practical guide to preparing company knowledge for an AI agent: real questions, source ownership, document structure, permissions, freshness and testing.
A practical guide to choosing between chatbots, workflow automation and AI agents for questions, predictable tasks and supervised action.
A practical checklist for choosing your first AI agent workflow: value, data, permissions, human review, testing and measurable outcomes.
Microsoft and Uber show why AI agent costs can spike when token usage scales. Learn why efficient agent design matters before bills run away.
The more work we hand to AI agents, the more our judgment and guardrails matter. Two principles for building agents you can actually trust - and a hands-on way to learn them.
AI agents are becoming the way work gets done. Learning to build one - even a simple one - is fast becoming a core skill for every professional, not just engineers. Here is why, and how to start.