Here's something I've been thinking about lately… An AI workflow runs successfully. No errors. The client gets the output. So why might the automation still be costing them more time than it saves? I came across G2's 2026 AI Agents report, and one finding caught my attention: 45% of surveyed B2B buyers said reviewing and correcting agent work was eating up more than a quarter of their expected productivity gains. That got me thinking about what we actually measure when we build automations. We check whether the workflow ran. But do we check how much of the output needed fixing? How many retries it took? How much human time was still involved? A green execution status doesn't necessarily mean a good result. One metric I'd like to see more builders track is cost per usable result, not just cost per run. When you build automations for clients, do you measure the quality of the final output?

Hey everyone! Business has been good, and we’re looking to scale. We have more incoming work than our core team can handle, so we’re looking for developers to take on some of our smaller projects. My consultancy is an official member of Anthropic’s Claude Partner Network, and our clients ask for Claude Certified developers. So we’ll give you a company email and sponsor your Claude Certified Architect – Foundations exam (normally US$125) as part of onboarding. The work: Project-based, at competitive rates. It typically involves integrating Claude into client systems and building automations. What we’re looking for: - Experience building with LLMs, ideally Claude, the API, Claude Code, or MCP - Comfortable working remotely and independently. Location and timezone don’t matter. - Willing to get CCA-F certified, which we’ll cover What you get: Paid client work, an official Anthropic certification for your resume, and room to take on bigger projects as we grow. We’re looking to onboard by the end of November, so reach out soon! DM me or comment below.