Tag: workflow-automation
How I run my whole consulting practice with Claude
I run Blue Sheen, the firm I lead with Pravina, through Claude and Claude Code. The practice lives in a version-controlled folder that Claude reads at the start of every session, with Close CRM as the source of truth. This is the real workflow stage by stage: prospecting, proposals, delivery, and the judgment a human still has to own.
When to use a dynamic workflow
A dynamic workflow in Claude Code runs up to sixteen subagents at once and a thousand across a job. That power is wasted on most tasks. This is the decision I use before reaching for one: when a single agent wins, when a dynamic workflow earns its cost, and when the answer is to not automate at all.
AI does tasks. It does not do jobs.
Ten years building Tallyfy, and a year pointing AI agents at it, taught me one blunt thing. A job is a chain of tasks, and AI reliability multiplies down that chain until the whole thing is a coin flip. The fix is not a smarter model.
Dynamic workflows: parallel verification at scale
Dynamic workflows in Claude Code run tens to hundreds of subagents that check each other before anything reaches you. The parallelism is not the interesting part. The verification is. Here is how I am using one to re-verify around 300 posts on this site, and when it earns its cost.
How to run Claude Code as non-interactive mini prompts for true 24/7 automation
Every article about Claude Code automation stays surface-level. Here is the production pattern for running non-interactive mini prompts with queue processing, quality gates, and auto-restart wrappers, built from running dozens of automated jobs across multiple repos every day.
Claude Artifacts for enterprise workflows - replacing expensive tools with AI
Mid-size companies spend tens of thousands annually on workflow tools that fragment their operations. Claude Artifacts offers a unified AI-powered workspace, with some teams achieving full ROI within three months.
The real AI assistant problem no one talks about
Everyone is jumping between ChatGPT, Claude, Gemini, and Perplexity, but constant AI assistant switching costs up to 40 percent of productive time and is destroying the very productivity gains AI was supposed to deliver
Notion AI vs Coda AI: Built-in beats bolted-on
Productiv data across 25,000 users shows Coda reaching 62.5% enterprise engagement versus 43.5% for Notion. Even after Notion 3.0 launched AI agents, structural AI integration still delivers better operational results for real workflows.
Self-driving workflows: when they work and when they fail
After multiple attempts at autonomous workflows, the pattern is clear - they work brilliantly for decisions, fail miserably for processes. Many agentic AI projects get cancelled before they ever reach production. As Beazley Insurance and Uber show, prerequisites matter more than technology.